Neuromorphic Chipsets - Industry Adoption AnalysisNetscribes
The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.
Based on patent data, this report analyzes the ongoing R&D and investments in neuromorphic chipsets by major institutions across the globe to reveal the top innovators and technology leaders in this space.
For the full report, contact info@netscribes.com
Visit www.netscribes.com
Nano computing describes computing that uses extremely small, or nano scale, devices. It will be integrated into existing products like disk drives and fundamentally new products, software, and architectures will be developed. Nano computing will change the computer industry in many ways by making existing technologies like memory and storage even more abundant and enabling new technologies to replace obsolete machines, requiring enormous effort and resources. Nano computers could potentially be small enough to fit in a jacket pocket while having storage for all of today's internet and processing capabilities surpassing today's supercomputers. However, full realization of nano computing's potential may not occur for at least 15 years.
- Acronis is a global leader in cyber protection with over 5.5 million prosumers and $250 million in revenue. It has dual headquarters in Switzerland and Singapore.
- The document discusses future computing technologies like quantum computing, photonic computing, brain-inspired computing and their potential to solve problems beyond the capabilities of classical computers. It also discusses challenges like fundamental physical limits, heat dissipation and the need for new materials and algorithms.
- A new research university called SIT is proposed to address global challenges through technology and innovation in areas like cybersecurity, AI, quantum technologies and new materials. It will be located in Schaffhausen, Switzerland near the Rhein Falls and partner with top universities
The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present.
There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped.
Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights.
Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs.
Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series
Many automakers are trying to utilize machine learning to realize automated driving of cars. Application of GPGPU and Approximate computing is being actively studied because using conventional CPUs in machine learning is often disadvantageous from the viewpoint of performance and energy consumption. As of today, they are in the stage of commercialization sufficiently. However, considering the high performance and low energy consumption required of automobiles that are several years ahead, it is not guaranteed enough that GPGPU and Approximate computing have the potential to fully satisfy them. Therefore, some automakers are considering Neuromophic device as a semiconductor candidate to be installed in the next generation of automatic driving vehicles. For the past eight months, IBM has been studying technologies for applying Japanese automobile manufacturers and Neuromophic devices to automobiles. We will report technical problems and application areas obtained from that study.
Jawar Singh is a professor at the Indian Institute of Technology Patna who researches devices and circuits for in-memory and brain-inspired computing. His presentation discusses the motivation for alternative computing architectures based on the remarkable efficiency of the human brain, which uses only 20W of power compared to supercomputers that require megawatts. It summarizes IBM's TrueNorth neuromorphic computing chip that emulates neural function with just 70mW of power. The presentation also describes Intel's Loihi neuromorphic processor and research using it for applications like robotics and database searches that consume less than 1W. Singh advocates for more energy efficient models of neurons and synapses for brain-inspired approaches and identifying the right memory
- Dynamic neural networks (DNNs) can adapt to varying resource availability on edge devices through techniques like incremental training and group convolution pruning. This allows meeting requirements for timing, power/energy, and accuracy.
- Experiments on two embedded platforms showed that dynamic DNNs combined with DVFS and task mapping can reduce energy consumption while maintaining classification accuracy compared to static DNNs.
- Runtime power management is needed to coordinate heterogeneous processors, respond to environmental factors, balance power consumption and battery life, and meet requirements for concurrently executing tasks and applications under varying conditions on edge devices.
Stories About Spark, HPC and Barcelona by Jordi TorresSpark Summit
HPC in Barcelona is centered around the MareNostrum supercomputer and BSC's 425-person team from 40 countries. MareNostrum allows simulation and analysis in fields like life sciences, earth sciences, and engineering. To meet new demands of big data analytics, BSC developed the Spark4MN module to run Spark workloads on MareNostrum. Benchmarking showed Spark4MN achieved good speed-up and scale-out. Further work profiles Spark using BSC tools and benchmarks workloads like image analysis on different hardware. BSC's vision is to advance understanding through technologies like cognitive computing and deep learning.
Neuromorphic Chipsets - Industry Adoption AnalysisNetscribes
The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.
Based on patent data, this report analyzes the ongoing R&D and investments in neuromorphic chipsets by major institutions across the globe to reveal the top innovators and technology leaders in this space.
For the full report, contact info@netscribes.com
Visit www.netscribes.com
Nano computing describes computing that uses extremely small, or nano scale, devices. It will be integrated into existing products like disk drives and fundamentally new products, software, and architectures will be developed. Nano computing will change the computer industry in many ways by making existing technologies like memory and storage even more abundant and enabling new technologies to replace obsolete machines, requiring enormous effort and resources. Nano computers could potentially be small enough to fit in a jacket pocket while having storage for all of today's internet and processing capabilities surpassing today's supercomputers. However, full realization of nano computing's potential may not occur for at least 15 years.
- Acronis is a global leader in cyber protection with over 5.5 million prosumers and $250 million in revenue. It has dual headquarters in Switzerland and Singapore.
- The document discusses future computing technologies like quantum computing, photonic computing, brain-inspired computing and their potential to solve problems beyond the capabilities of classical computers. It also discusses challenges like fundamental physical limits, heat dissipation and the need for new materials and algorithms.
- A new research university called SIT is proposed to address global challenges through technology and innovation in areas like cybersecurity, AI, quantum technologies and new materials. It will be located in Schaffhausen, Switzerland near the Rhein Falls and partner with top universities
The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present.
There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped.
Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights.
Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs.
Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series
Many automakers are trying to utilize machine learning to realize automated driving of cars. Application of GPGPU and Approximate computing is being actively studied because using conventional CPUs in machine learning is often disadvantageous from the viewpoint of performance and energy consumption. As of today, they are in the stage of commercialization sufficiently. However, considering the high performance and low energy consumption required of automobiles that are several years ahead, it is not guaranteed enough that GPGPU and Approximate computing have the potential to fully satisfy them. Therefore, some automakers are considering Neuromophic device as a semiconductor candidate to be installed in the next generation of automatic driving vehicles. For the past eight months, IBM has been studying technologies for applying Japanese automobile manufacturers and Neuromophic devices to automobiles. We will report technical problems and application areas obtained from that study.
Jawar Singh is a professor at the Indian Institute of Technology Patna who researches devices and circuits for in-memory and brain-inspired computing. His presentation discusses the motivation for alternative computing architectures based on the remarkable efficiency of the human brain, which uses only 20W of power compared to supercomputers that require megawatts. It summarizes IBM's TrueNorth neuromorphic computing chip that emulates neural function with just 70mW of power. The presentation also describes Intel's Loihi neuromorphic processor and research using it for applications like robotics and database searches that consume less than 1W. Singh advocates for more energy efficient models of neurons and synapses for brain-inspired approaches and identifying the right memory
- Dynamic neural networks (DNNs) can adapt to varying resource availability on edge devices through techniques like incremental training and group convolution pruning. This allows meeting requirements for timing, power/energy, and accuracy.
- Experiments on two embedded platforms showed that dynamic DNNs combined with DVFS and task mapping can reduce energy consumption while maintaining classification accuracy compared to static DNNs.
- Runtime power management is needed to coordinate heterogeneous processors, respond to environmental factors, balance power consumption and battery life, and meet requirements for concurrently executing tasks and applications under varying conditions on edge devices.
Stories About Spark, HPC and Barcelona by Jordi TorresSpark Summit
HPC in Barcelona is centered around the MareNostrum supercomputer and BSC's 425-person team from 40 countries. MareNostrum allows simulation and analysis in fields like life sciences, earth sciences, and engineering. To meet new demands of big data analytics, BSC developed the Spark4MN module to run Spark workloads on MareNostrum. Benchmarking showed Spark4MN achieved good speed-up and scale-out. Further work profiles Spark using BSC tools and benchmarks workloads like image analysis on different hardware. BSC's vision is to advance understanding through technologies like cognitive computing and deep learning.
How HPC and large-scale data analytics are transforming experimental scienceinside-BigData.com
In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science.
"Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale."
Watch the video: https://wp.me/p3RLHQ-kLV
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The document discusses future frameworks and techniques for analyzing potential futures, including short and long-term forecasting methods. It covers several frameworks for conceptualizing technologies and their development over time, including level of technology, dimensional models, chronological models, and paradigms of growth. Key drivers of technological change and the concept of discontinuities and adjacent advances are also examined.
Arm A64fx and Post-K: Game-Changing CPU & Supercomputer for HPC, Big Data, & AIinside-BigData.com
Satoshi Matsuoka from RIKEN gave this talk at the HPC User Forum in Santa Fe.
"With rapid rise and increase of Big Data and AI as a new breed of high-performance workloads on supercomputers, we need to accommodate them at scale, and thus the need for R&D for HW and SW Infrastructures where traditional simulation-based HPC and BD/AI would converge, in a BYTES-oriented fashion. Post-K is the flagship next generation national supercomputer being developed by Riken and Fujitsu in collaboration. Post-K will have hyperscale class resource in one exascale machine, with well more than 100,000 nodes of sever-class A64fx many-core Arm CPUs, realized through extensive co-design process involving the entire Japanese HPC community.
Rather than to focus on double precision flops that are of lesser utility, rather Post-K, especially its Arm64fx processor and the Tofu-D network is designed to sustain extreme bandwidth on realistic applications including those for oil and gas, such as seismic wave propagation, CFD, as well as structural codes, besting its rivals by several factors in measured performance. Post-K is slated to perform 100 times faster on some key applications c.f. its predecessor, the K-Computer, but also will likely to be the premier big data and AI/ML infrastructure. Currently, we are conducting research to scale deep learning to more than 100,000 nodes on Post-K, where we would obtain near top GPU-class performance on each node."
Watch the video: https://wp.me/p3RLHQ-k6G
Learn more: https://en.wikichip.org/wiki/supercomputers/post-k
and
http://hpcuserforum.com
The document discusses the future of high performance computing (HPC). It covers several topics:
- Next generation HPC applications will involve larger problems in fields like disaster simulation, urban science, and data-intensive science. Projects like the Square Kilometer Array will generate exabytes of data daily.
- Hardware trends include using many-core processors, accelerators like GPUs, and heterogeneous computing with CPUs and GPUs. Future exascale systems may use conventional CPUs with GPUs or innovative architectures like Japan's Post-K system.
- The top supercomputers in the world currently include Summit, a IBM system combining Power9 CPUs and Nvidia Voltas at Oak Ridge, and China's Sunway Taihu
This document provides an overview of neuromorphic computing. It begins with an abstract that outlines some of the key aspects of neuromorphic systems, including their novel solutions for artificial intelligence compared to von Neumann architectures. The introduction describes how neuromorphic chips could power neuromorphic computers to perform tasks like deep learning. The existing von Neumann system is described as having separate processing and memory units, binary encoding, and speed/energy issues due to data movement. The proposed neuromorphic system aims to have collocated processing and memory, massive parallelism, scalability, event-driven computation, adaptability, and fault tolerance. The document outlines applications in medicine, operations, AI, and imaging and advantages like real-time computation and
The document summarizes emerging computing trends in data centers, including:
1) The shift to multi-core CPU designs after Dennard scaling broke down, driven by the need for energy efficient designs for cloud computing.
2) The rise of heterogeneous computing using application-specific accelerators like GPUs and FPGAs to improve efficiency for targeted workloads like machine learning.
3) How technologies developed for mobile and edge computing like ARM cores can improve data center server efficiency through typical-use optimization rather than just peak performance.
HKG18-500K1 - Keynote: Dileep Bhandarkar - Emerging Computing Trends in the D...Linaro
Session ID: HKG18-500K1
Session Name: HKG18-500K1 - Keynote: Dileep Bhandarkar - Emerging Computing Trends in the Datacenter
Speaker: Not Available
Track: Keynote
★ Session Summary ★
For decades we have been able to take advantage of Moore’s Law to improve single thread performance, reduce power and cost with each generation of semiconductor technology. While technology has advanced after the end of Dennard scaling more than 10 years ago, the advances have slowed down. Server performance increases have relied on increasing core counts and power budgets.
At the same time, workloads have changed in the era of cloud computing. Scale out is becoming more important than scale up. Domain specific architectures have started to emerge to improve the energy efficiency of emerging workloads like deep learning
This talk will provide a historical perspective and discuss emerging trends driving the development of modern servers processors.
---------------------------------------------------
★ Resources ★
Event Page: http://connect.linaro.org/resource/hkg18/hkg18-500k1/
Presentation: http://connect.linaro.org.s3.amazonaws.com/hkg18/presentations/hkg18-500k1.pdf
Video: http://connect.linaro.org.s3.amazonaws.com/hkg18/videos/hkg18-500k1.mp4
---------------------------------------------------
★ Event Details ★
Linaro Connect Hong Kong 2018 (HKG18)
19-23 March 2018
Regal Airport Hotel Hong Kong
---------------------------------------------------
Keyword: Keynote
'http://www.linaro.org'
'http://connect.linaro.org'
---------------------------------------------------
Follow us on Social Media
https://www.facebook.com/LinaroOrg
https://www.youtube.com/user/linaroorg?sub_confirmation=1
https://www.linkedin.com/company/1026961
For decades we have been able to take advantage of Moore’s Law to improve single thread performance, reduce power and cost with each generation of semiconductor technology. While technology has advanced after the end of Dennard scaling more than 10 years ago, the advances have slowed down. Server performance increases have relied on increasing core counts and power budgets.
At the same time, workloads have changed in the era of cloud computing. Scale out is becoming more important than scale up. Domain specific architectures have started to emerge to improve the energy efficiency of emerging workloads like deep learning.
This talk will provide a historical perspective and discuss emerging trends driving the development of modern processors.
Application Optimized Performance: Choosing the Right Instance (CPN212) | AWS...Amazon Web Services
(Presented by Intel)
Each application places a different set of requirements on the underlying infrastructure.
Whether it is web, big data analytics, technical computing, or general enterprise applications, applications are run more efficiently when performance, IO bandwidth, and memory capacity have been custom-tailored for that specific application.
Jason Waxman, GM and VP of Intel’s Cloud Platform Group, looks under the hood at the different types of processors that comprise Amazon Web Services instances and shares insights from Intel IT and industry best practices for right-sizing infrastructure for different application characteristics and capabilities. By leveraging the underlying performance, security capabilities, and flexibility of various instance types, developers can more easily migrate applications into the cloud and drive down TCO for cloud-based services.
A New Direction for Computer Architecture Researchdbpublications
This paper we suggest a different computing environment as a worthy new direction for computer architecture research: personal mobile computing, where portable devices are used for visual computing and personal communications tasks. Such a device supports in an integrated fashion all the functions provided to-day by a portable computer, a cellular phone, a digital camera and a video game. The requirements placed on the processor in this environment are energy efficiency, high performance for multimedia and DSP functions, and area efficient, scalable designs. We examine the architectures that were recently pro-posed for billion transistor microprocessors. While they are very promising for the stationary desktop and server workloads, we discover that most of them are un-able to meet the challenges of the new environment and provide the necessary enhancements for multimedia applications running on portable devices.
Holographic optical data storage jyoti-225Charu Tyagi
Holographic Optical Data Storage (HODS) is a revolutionary data storage technology that uses holograms rather than bits to store large volumes of data. It works by using lasers and optical materials to record images as interference patterns in a photosensitive medium. This allows for massive storage capacities - a 1cm3 cube could store the equivalent of thousands of DVDs or hard drives. While researched since the 1960s, HODS is now gaining momentum as a solution to handle growing storage needs. It promises faster access and greater densities than existing magnetic and optical storage, positioning it to potentially replace those methods altogether in the future.
New Business Applications Powered by In-Memory Technology @MIT Forum for Supp...Paul Hofmann
Talk about 4 leading edge projects:
1) Optimal pricing for energy management, online pricing, and truck scheduling @Princeton University
2) Infinite DRAM - RAMCloud @Stanford University
Applications: Extremely low latency and very high bandwidth
a) Facebook like problems with high read AND write rate,
b) advanced analytics, c) what-if scenarios for demand planning
3) Hybrid In-Memory Store @MIT CSAIL
4) Multithreading Real Time Event Platform @MIT Auto-ID Lab
500k events/s and millions of threads in-memory or distributed used for automatic meter reading, online billing, mobile billing and Smart Grid
In this video from ChefConf 2014 in San Francisco, Cycle Computing CEO Jason Stowe outlines the biggest challenge facing us today, Climate Change, and suggests how Cloud HPC can help find a solution, including ideas around Climate Engineering, and Renewable Energy.
"As proof points, Jason uses three use cases from Cycle Computing customers, including from companies like HGST (a Western Digital Company), Aerospace Corporation, Novartis, and the University of Southern California. It’s clear that with these new tools that leverage both Cloud Computing, and HPC – the power of Cloud HPC enables researchers, and designers to ask the right questions, to help them find better answers, faster. This all delivers a more powerful future, and means to solving these really difficult problems."
Watch the video presentation: http://insidehpc.com/2014/09/video-hpc-cluster-computing-64-156000-cores/
In this video from the Stanford HPC Conference, Liran Zvibel from Weka.IO presents: Making Machine Learning Compute Bound Again.
"GPUs are getting faster on a yearly cycle. Networking was able to catch up and support linear scaling of models that fit in memory. Traditional storage has not caught up to the condensed performance needed by GPU-filled servers. The amount of concurrent clients and the sheer amount of data required to effectively scale modern deep learning models keeps growing.
We are going to present WekaIO, the lowest latency, highest throughput file system solution that scales to 100s of PB in a single namespace supporting the most challenging deep learning projects that run today. We will present real life benchmarks comparing WekaIO performance to a local SSD file system, showing that we are the only coherent shared storage that is even faster than the current caching solutons, while allowing customers to linearly scale performance by adding more GPU servers. Also, we will view the complete ML project lifecycle, from collecting data, cleaning, tagging, exploring, training, validating, and finally archiving, and how customers can use cloud bursting to leverage public cloud infrastructure for improved economics."
Learn more: https://weka.io
and
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This slide explains about the detailed view hardware architecture which includes CPUs, GPUs, Interconnect networks and applications used by the summit supercomputer
We envision a world where devices, machines, automobiles, and things are much more intelligent, simplifying and enriching our daily lives. They will be able to perceive, reason, and take intuitive actions based on awareness of the situation, improving just about any experience and solving problems that to this point we’ve either left to the user, or to more conventional algorithms.
Artificial intelligence (AI) is the technology driving this revolution. You may think that AI is really about big data and the cloud, and yet Qualcomm’s solutions already have the power, thermal, and processing efficiency to run powerful AI algorithms on the actual device. Our current products now support many AI use cases, such as computer vision, natural language processing, and malware detection — both for smartphones and autos — and we are researching broader topics, such as AI for wireless connectivity, power management, and photography. View this presentation to learn about our AI vision, including:
Why mobile is becoming the pervasive AI platform
The benefits of AI moving to the device and complementing the cloud
The benefits of distributed processing for AI
Qualcomm’s long history of AI research and development
What the future of AI processing might look like
The document discusses EdgeAI, a startup developing an AI chip with a custom machine learning accelerator and new embedded memory technologies targeting low-power, high-performance edge applications. In early stages, EdgeAI aimed to enable AI vision on battery-powered cameras but faced challenges competing with GPUs. It later found product-market fit enabling solar-powered security cameras by developing a chip that performs inference with no idle power consumption. EdgeAI will validate this approach with pilots and plans to fabricate a second silicon chip and raise seed funding to develop end-to-end prototypes.
This document discusses the history and future of the Internet and Internet of Things (IoT). It provides a brief history of the early ARPANET project in the 1960s and the development of the first Internet connection in Korea in 1982. It covers the creation of the World Wide Web in 1989 and the first web browser in 1993 that made the Internet accessible to non-experts. The document also discusses standards for IoT connectivity including 6LoWPAN, RPL, and CoAP and how they enable IPv6 connectivity for constrained devices. Finally, it introduces the SNAIL project for providing IPv6 connectivity to small, low-power embedded devices in an IoT context.
How HPC and large-scale data analytics are transforming experimental scienceinside-BigData.com
In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science.
"Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale."
Watch the video: https://wp.me/p3RLHQ-kLV
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The document discusses future frameworks and techniques for analyzing potential futures, including short and long-term forecasting methods. It covers several frameworks for conceptualizing technologies and their development over time, including level of technology, dimensional models, chronological models, and paradigms of growth. Key drivers of technological change and the concept of discontinuities and adjacent advances are also examined.
Arm A64fx and Post-K: Game-Changing CPU & Supercomputer for HPC, Big Data, & AIinside-BigData.com
Satoshi Matsuoka from RIKEN gave this talk at the HPC User Forum in Santa Fe.
"With rapid rise and increase of Big Data and AI as a new breed of high-performance workloads on supercomputers, we need to accommodate them at scale, and thus the need for R&D for HW and SW Infrastructures where traditional simulation-based HPC and BD/AI would converge, in a BYTES-oriented fashion. Post-K is the flagship next generation national supercomputer being developed by Riken and Fujitsu in collaboration. Post-K will have hyperscale class resource in one exascale machine, with well more than 100,000 nodes of sever-class A64fx many-core Arm CPUs, realized through extensive co-design process involving the entire Japanese HPC community.
Rather than to focus on double precision flops that are of lesser utility, rather Post-K, especially its Arm64fx processor and the Tofu-D network is designed to sustain extreme bandwidth on realistic applications including those for oil and gas, such as seismic wave propagation, CFD, as well as structural codes, besting its rivals by several factors in measured performance. Post-K is slated to perform 100 times faster on some key applications c.f. its predecessor, the K-Computer, but also will likely to be the premier big data and AI/ML infrastructure. Currently, we are conducting research to scale deep learning to more than 100,000 nodes on Post-K, where we would obtain near top GPU-class performance on each node."
Watch the video: https://wp.me/p3RLHQ-k6G
Learn more: https://en.wikichip.org/wiki/supercomputers/post-k
and
http://hpcuserforum.com
The document discusses the future of high performance computing (HPC). It covers several topics:
- Next generation HPC applications will involve larger problems in fields like disaster simulation, urban science, and data-intensive science. Projects like the Square Kilometer Array will generate exabytes of data daily.
- Hardware trends include using many-core processors, accelerators like GPUs, and heterogeneous computing with CPUs and GPUs. Future exascale systems may use conventional CPUs with GPUs or innovative architectures like Japan's Post-K system.
- The top supercomputers in the world currently include Summit, a IBM system combining Power9 CPUs and Nvidia Voltas at Oak Ridge, and China's Sunway Taihu
This document provides an overview of neuromorphic computing. It begins with an abstract that outlines some of the key aspects of neuromorphic systems, including their novel solutions for artificial intelligence compared to von Neumann architectures. The introduction describes how neuromorphic chips could power neuromorphic computers to perform tasks like deep learning. The existing von Neumann system is described as having separate processing and memory units, binary encoding, and speed/energy issues due to data movement. The proposed neuromorphic system aims to have collocated processing and memory, massive parallelism, scalability, event-driven computation, adaptability, and fault tolerance. The document outlines applications in medicine, operations, AI, and imaging and advantages like real-time computation and
The document summarizes emerging computing trends in data centers, including:
1) The shift to multi-core CPU designs after Dennard scaling broke down, driven by the need for energy efficient designs for cloud computing.
2) The rise of heterogeneous computing using application-specific accelerators like GPUs and FPGAs to improve efficiency for targeted workloads like machine learning.
3) How technologies developed for mobile and edge computing like ARM cores can improve data center server efficiency through typical-use optimization rather than just peak performance.
HKG18-500K1 - Keynote: Dileep Bhandarkar - Emerging Computing Trends in the D...Linaro
Session ID: HKG18-500K1
Session Name: HKG18-500K1 - Keynote: Dileep Bhandarkar - Emerging Computing Trends in the Datacenter
Speaker: Not Available
Track: Keynote
★ Session Summary ★
For decades we have been able to take advantage of Moore’s Law to improve single thread performance, reduce power and cost with each generation of semiconductor technology. While technology has advanced after the end of Dennard scaling more than 10 years ago, the advances have slowed down. Server performance increases have relied on increasing core counts and power budgets.
At the same time, workloads have changed in the era of cloud computing. Scale out is becoming more important than scale up. Domain specific architectures have started to emerge to improve the energy efficiency of emerging workloads like deep learning
This talk will provide a historical perspective and discuss emerging trends driving the development of modern servers processors.
---------------------------------------------------
★ Resources ★
Event Page: http://connect.linaro.org/resource/hkg18/hkg18-500k1/
Presentation: http://connect.linaro.org.s3.amazonaws.com/hkg18/presentations/hkg18-500k1.pdf
Video: http://connect.linaro.org.s3.amazonaws.com/hkg18/videos/hkg18-500k1.mp4
---------------------------------------------------
★ Event Details ★
Linaro Connect Hong Kong 2018 (HKG18)
19-23 March 2018
Regal Airport Hotel Hong Kong
---------------------------------------------------
Keyword: Keynote
'http://www.linaro.org'
'http://connect.linaro.org'
---------------------------------------------------
Follow us on Social Media
https://www.facebook.com/LinaroOrg
https://www.youtube.com/user/linaroorg?sub_confirmation=1
https://www.linkedin.com/company/1026961
For decades we have been able to take advantage of Moore’s Law to improve single thread performance, reduce power and cost with each generation of semiconductor technology. While technology has advanced after the end of Dennard scaling more than 10 years ago, the advances have slowed down. Server performance increases have relied on increasing core counts and power budgets.
At the same time, workloads have changed in the era of cloud computing. Scale out is becoming more important than scale up. Domain specific architectures have started to emerge to improve the energy efficiency of emerging workloads like deep learning.
This talk will provide a historical perspective and discuss emerging trends driving the development of modern processors.
Application Optimized Performance: Choosing the Right Instance (CPN212) | AWS...Amazon Web Services
(Presented by Intel)
Each application places a different set of requirements on the underlying infrastructure.
Whether it is web, big data analytics, technical computing, or general enterprise applications, applications are run more efficiently when performance, IO bandwidth, and memory capacity have been custom-tailored for that specific application.
Jason Waxman, GM and VP of Intel’s Cloud Platform Group, looks under the hood at the different types of processors that comprise Amazon Web Services instances and shares insights from Intel IT and industry best practices for right-sizing infrastructure for different application characteristics and capabilities. By leveraging the underlying performance, security capabilities, and flexibility of various instance types, developers can more easily migrate applications into the cloud and drive down TCO for cloud-based services.
A New Direction for Computer Architecture Researchdbpublications
This paper we suggest a different computing environment as a worthy new direction for computer architecture research: personal mobile computing, where portable devices are used for visual computing and personal communications tasks. Such a device supports in an integrated fashion all the functions provided to-day by a portable computer, a cellular phone, a digital camera and a video game. The requirements placed on the processor in this environment are energy efficiency, high performance for multimedia and DSP functions, and area efficient, scalable designs. We examine the architectures that were recently pro-posed for billion transistor microprocessors. While they are very promising for the stationary desktop and server workloads, we discover that most of them are un-able to meet the challenges of the new environment and provide the necessary enhancements for multimedia applications running on portable devices.
Holographic optical data storage jyoti-225Charu Tyagi
Holographic Optical Data Storage (HODS) is a revolutionary data storage technology that uses holograms rather than bits to store large volumes of data. It works by using lasers and optical materials to record images as interference patterns in a photosensitive medium. This allows for massive storage capacities - a 1cm3 cube could store the equivalent of thousands of DVDs or hard drives. While researched since the 1960s, HODS is now gaining momentum as a solution to handle growing storage needs. It promises faster access and greater densities than existing magnetic and optical storage, positioning it to potentially replace those methods altogether in the future.
New Business Applications Powered by In-Memory Technology @MIT Forum for Supp...Paul Hofmann
Talk about 4 leading edge projects:
1) Optimal pricing for energy management, online pricing, and truck scheduling @Princeton University
2) Infinite DRAM - RAMCloud @Stanford University
Applications: Extremely low latency and very high bandwidth
a) Facebook like problems with high read AND write rate,
b) advanced analytics, c) what-if scenarios for demand planning
3) Hybrid In-Memory Store @MIT CSAIL
4) Multithreading Real Time Event Platform @MIT Auto-ID Lab
500k events/s and millions of threads in-memory or distributed used for automatic meter reading, online billing, mobile billing and Smart Grid
In this video from ChefConf 2014 in San Francisco, Cycle Computing CEO Jason Stowe outlines the biggest challenge facing us today, Climate Change, and suggests how Cloud HPC can help find a solution, including ideas around Climate Engineering, and Renewable Energy.
"As proof points, Jason uses three use cases from Cycle Computing customers, including from companies like HGST (a Western Digital Company), Aerospace Corporation, Novartis, and the University of Southern California. It’s clear that with these new tools that leverage both Cloud Computing, and HPC – the power of Cloud HPC enables researchers, and designers to ask the right questions, to help them find better answers, faster. This all delivers a more powerful future, and means to solving these really difficult problems."
Watch the video presentation: http://insidehpc.com/2014/09/video-hpc-cluster-computing-64-156000-cores/
In this video from the Stanford HPC Conference, Liran Zvibel from Weka.IO presents: Making Machine Learning Compute Bound Again.
"GPUs are getting faster on a yearly cycle. Networking was able to catch up and support linear scaling of models that fit in memory. Traditional storage has not caught up to the condensed performance needed by GPU-filled servers. The amount of concurrent clients and the sheer amount of data required to effectively scale modern deep learning models keeps growing.
We are going to present WekaIO, the lowest latency, highest throughput file system solution that scales to 100s of PB in a single namespace supporting the most challenging deep learning projects that run today. We will present real life benchmarks comparing WekaIO performance to a local SSD file system, showing that we are the only coherent shared storage that is even faster than the current caching solutons, while allowing customers to linearly scale performance by adding more GPU servers. Also, we will view the complete ML project lifecycle, from collecting data, cleaning, tagging, exploring, training, validating, and finally archiving, and how customers can use cloud bursting to leverage public cloud infrastructure for improved economics."
Learn more: https://weka.io
and
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This slide explains about the detailed view hardware architecture which includes CPUs, GPUs, Interconnect networks and applications used by the summit supercomputer
We envision a world where devices, machines, automobiles, and things are much more intelligent, simplifying and enriching our daily lives. They will be able to perceive, reason, and take intuitive actions based on awareness of the situation, improving just about any experience and solving problems that to this point we’ve either left to the user, or to more conventional algorithms.
Artificial intelligence (AI) is the technology driving this revolution. You may think that AI is really about big data and the cloud, and yet Qualcomm’s solutions already have the power, thermal, and processing efficiency to run powerful AI algorithms on the actual device. Our current products now support many AI use cases, such as computer vision, natural language processing, and malware detection — both for smartphones and autos — and we are researching broader topics, such as AI for wireless connectivity, power management, and photography. View this presentation to learn about our AI vision, including:
Why mobile is becoming the pervasive AI platform
The benefits of AI moving to the device and complementing the cloud
The benefits of distributed processing for AI
Qualcomm’s long history of AI research and development
What the future of AI processing might look like
The document discusses EdgeAI, a startup developing an AI chip with a custom machine learning accelerator and new embedded memory technologies targeting low-power, high-performance edge applications. In early stages, EdgeAI aimed to enable AI vision on battery-powered cameras but faced challenges competing with GPUs. It later found product-market fit enabling solar-powered security cameras by developing a chip that performs inference with no idle power consumption. EdgeAI will validate this approach with pilots and plans to fabricate a second silicon chip and raise seed funding to develop end-to-end prototypes.
This document discusses the history and future of the Internet and Internet of Things (IoT). It provides a brief history of the early ARPANET project in the 1960s and the development of the first Internet connection in Korea in 1982. It covers the creation of the World Wide Web in 1989 and the first web browser in 1993 that made the Internet accessible to non-experts. The document also discusses standards for IoT connectivity including 6LoWPAN, RPL, and CoAP and how they enable IPv6 connectivity for constrained devices. Finally, it introduces the SNAIL project for providing IPv6 connectivity to small, low-power embedded devices in an IoT context.
Similar to Weebit nano presentation at Leti Memory Workshop (20)
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
1. Weebit Nano (ASX: WBT)
Silicon Oxide ReRAM Technology
1
Amir Regev
VP R&D
June 2017
2. 2
Weebit-nano Fast Facts
Re-listed on the ASX
in August 2016
HQ in Israel, R&D in
France via Leti
Developing next-gen memory
solution based on Silicon
Oxide (SiOx) ReRAM
Targeting embedded, storage and
next generations markets
Business and technological
Partners: CEA/Leti – France,
Rice University - USA
Several US Patents
Our Mission is to Bring to the world a highly
manufacturable high performing ReRAM technology
3. Weebit Nano Leadership Team
3
David (Dadi) Perlmutter
Chairman
Ex-Intel EVP
IEEE Fellow
Has led intel into
the Data Center
Brought to Market:
Centrino™ mobile technology
Prof. James Tour
Inventor
Scientist of the Year 2013
R&D magazine
Inducted to the National
Academy of inventors
Feynman prize in
Nano science
Yossi Keret
CEO
Extensive management and
financial experience
Financially led a variety of
international companies
Experience in equity raisings
for public companies
Amir Regev
VP R&D
45nm NOR Flash Technology
Development at Micron
Two decades in
Semiconductors engineering
Was part of Automotive
division at Intel
4. Weebit Confidential
2020
40 ZB
2015
7.91 ZB
“Insatiable demand for data”
World memory storage use is growing
exponentially
The world is becoming increasingly
desperate for a high performance
memory device.
*1 zettabyte = 1012 gigabytes
2012
2.72 ZB
Global digital data
6. 6
Power Consumption
In 2014, data centers in the U.S. consumed an estimated 70 billion kWh, representing about 2% of
total U.S. electricity consumption
By 2020 US will need another 17 power plants to meet storage demand*
*Patrick Thibodeau COMPUTERWORLD
Energy efficiency is not nice to have – it’s a MUST!
Data centers are becoming the new polluters
7. Artificial Intelligence has entered our lives
Man VS. Machine - IBM's Watson Supercomputer Destroys Humans in Jeopardy
60%-70% of the IBM
cloud customers are
using Watson AI
From mobile first
to AI first
8. Computer (IBM Watson) VS. Human Brain
Human Brain
• 1011 Neurons 1015 Synapses
• Volume - 2 liter
• Power - 10 Watt
• Frequency – 4, 8, 40Hz
• Event Driven
• 2 Kg brain weight
IBM Watson supercomputer
• 2880 computing cores (90*8*4)
• Volume - 10 refrigerators in size
• Power - 80 kW
• Frequency –> 3.5GHz
• Memory – 16TB RAM (not HDD!)
• 20 tones of air-conditioned cooling capacity
9. Biological brain benefits:
Massively parallel
Three-dimensionally organized and extremely compact
Extremely Power efficient
Combines storage and computation
Fault and variation tolerant
Self-learning and adaptive to changing environments
Biological brain – much more efficient computing architecture
10. Brain-inspired neuromorphic computation aims
10
Emulate the brain Instead of simulate the brain
The ability to mimic the biological computation at
the synaptic level will be a big step forward toward
building massively parallel computational systems
ReRAM has been identified as a potential
synapse physical and behavioral similarities
Ions migration leads to resistivity modulation Ions migration leads to resistivity modulation
11. Why neuromorphic computing
11
Neuromorphic computing:
• Mimic neuro-bio architecture of nervous system
• highly energy efficient - Asynchronous event driven algorithms
• Localization of the memory and processing units synapse and neurons
Conventional computing:
• Already facing scaling challenge (Moore's law)
• Excessive power consumption – 4-6 orders of magnitude than the brain
• Physical separation between CPU and memory – Von Neuman bottleneck
12. Basic Concept – From Neurons to learning
12
o 1011 neurons and, 1015 synapses - Each neuron connected through 1000–10,000 synapses
o Hebb's Law (1949) - ‘Neurons that fire together, wire together’
Basic Neuron Pre-Post Neuron Synapse Hebbian Learning Plasticity
13. Basic Concept – From ReRAM to online learning
13
o STDP events - Spike Time Dependent Plasticity
o SRDP - Spike-Rate Dependent Plasticity
o LIF – Leaky Integrate and Fire - Integration of Spikes
Basic Memristive Synapse SRDP concept Online unsupervised learning
S. Ambrogio, et al., Nanotechnology 24 (2013)
14. ReRAM Technology for Tomorrow
14
Mimic the brain as accurately as possible
o Incredible ReRAM resembles to Neurons
biological synapse
o Physical similarities leads to functional similarities
o Highly energy efficient
Which make it an enabler to Brain Inspired Artificial
Intelligence systems using ReRAM
ReRAM is the solution for tomorrow’s needs Achieving artificial intelligence capabilities
Machine
learning
Object
recognition
Brain inspired
computing systems
15. ReRAM Technology Development Today
15
Energy Efficiency
Ultra-low Power in pJ range
Speed
Fast programming 100-1000 faster than Flash
Integration
Bringing the memory closer to the processor
Low Cost
Manufacturability - minimum added process steps and cost
ReRAM designed for the Next-Gen memory:
16. 16
Manufacturability
ReRAM Not Used in Semiconductor Fabs Used in Semiconductor Fabs
Weebit nano SiOx ReRAM –manufacturability oriented:
Silicon Oxide – Weebit nano Next-Gen memory solution:
o Fab Friendly – ½ Century Process and Manufacturing experience
o Compatibility – well integrates with existing proven processes
o High Bandgap – Large memory window
Silicon Oxide – manufacturable anywhere:
✓ Any Fab – no need for specialized foundry
✓ Any Tool – no need for special tool
✓ Any process – no need for special process
Silicon Oxide- shortest time to market
17. Summary
17
Silicon Oxide- shortest time to market
ReRAM technology Addressing
exponentially growing memory
market
New opportunities emerging in
cognitive Artificial Intelligence
systems
Weebit Partnership with Leti - a
world leading research institute
Manufacturability is a key issue
in adopting new technology
Energy efficiency is the key in every
aspect of devices and cloud