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Music therapy
The document discusses the history and modern uses of music therapy. Music therapy uses music to help communication, learning, and expression for patients and groups. Ancient Egyptians and Greeks used music for its curative properties. Today, music therapy improves physical, psychological, intellectual, and social functioning for people with health or educational issues, including children, adults, seniors, and those without illness. It can help explore feelings, change moods, develop control, and learn skills. Music influences breathing, blood pressure, muscle coordination, and temperature in ways that reduce stress and tension. Music therapy treats conditions like learning disabilities, conduct issues, autism, deficiencies, socialization difficulties, low self-esteem, and age-related or chronic diseases
Why fpga
In this second presentation we are going to point out the reason why FPGA is the best technology for the NECSTrade's project.
DeepLearningAlgorithmAccelerationOnHardwarePlatforms_V2.0
Team 6 is comprised of 5 members: Sourabh Ketkale, Sahil Kaw, Siddhi Pai, Goutham Nekkalapu, and Prince Jacob Chandy. The document discusses several techniques for optimizing neural network performance on different hardware, including using 8-bit quantization, SSE3 and SSE4 instruction sets, batching, lazy evaluation, batched lazy evaluation, and implementing neural networks on the Xeon Phi processor using techniques such as data parallelism and task parallelism. It also discusses using FPGAs and distributed systems to achieve large-scale deep learning.
Recommended
Music therapy
The document discusses the history and modern uses of music therapy. Music therapy uses music to help communication, learning, and expression for patients and groups. Ancient Egyptians and Greeks used music for its curative properties. Today, music therapy improves physical, psychological, intellectual, and social functioning for people with health or educational issues, including children, adults, seniors, and those without illness. It can help explore feelings, change moods, develop control, and learn skills. Music influences breathing, blood pressure, muscle coordination, and temperature in ways that reduce stress and tension. Music therapy treats conditions like learning disabilities, conduct issues, autism, deficiencies, socialization difficulties, low self-esteem, and age-related or chronic diseases
Why fpga
In this second presentation we are going to point out the reason why FPGA is the best technology for the NECSTrade's project.
DeepLearningAlgorithmAccelerationOnHardwarePlatforms_V2.0
Team 6 is comprised of 5 members: Sourabh Ketkale, Sahil Kaw, Siddhi Pai, Goutham Nekkalapu, and Prince Jacob Chandy. The document discusses several techniques for optimizing neural network performance on different hardware, including using 8-bit quantization, SSE3 and SSE4 instruction sets, batching, lazy evaluation, batched lazy evaluation, and implementing neural networks on the Xeon Phi processor using techniques such as data parallelism and task parallelism. It also discusses using FPGAs and distributed systems to achieve large-scale deep learning.
Machine Learning with New Hardware Challegens
Describe basic neural network design and focus on Convolutional Neural Network architecture. Explain why CPU and GPU can't fulfill CNN hardware requirement. List out three hardware examples: Nvidia, Microsoft and Google. Finally highlight optimization approach for CNN design.
"Trade-offs in Implementing Deep Neural Networks on FPGAs," a Presentation fr...
"Trade-offs in Implementing Deep Neural Networks on FPGAs," a Presentation fr...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them."Accelerating Deep Learning Using Altera FPGAs," a Presentation from Intel
"Accelerating Deep Learning Using Altera FPGAs," a Presentation from IntelEdge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Bill Jenkins, Senior Product Specialist for High Level Design Tools at Intel, presents the "Accelerating Deep Learning Using Altera FPGAs" tutorial at the May 2016 Embedded Vision Summit.
While large strides have recently been made in the development of high-performance systems for neural networks based on multi-core technology, significant challenges in power, cost and, performance scaling remain. Field-programmable gate arrays (FPGAs) are a natural choice for implementing neural networks because they can combine computing, logic, and memory resources in a single device. Intel's Programmable Solutions Group has developed a scalable convolutional neural network reference design for deep learning systems using the OpenCL programming language built with our SDK for OpenCL. The design performance is being benchmarked using several popular CNN benchmarks: CIFAR-10, ImageNet and KITTI.
Building the CNN with OpenCL kernels allows true scaling of the design from smaller to larger devices and from one device generation to the next. New designs can be sized using different numbers of kernels at each layer. Performance scaling from one generation to the next also benefits from architectural advancements, such as floating-point engines and frequency scaling. Thus, you achieve greater than linear performance and performance per watt scaling with each new series of devices.FPGAで作るOpenFlow Switch （FPGAエクストリーム・コンピューティング 第6回） FPGAX#6
OpenFlow Switchは従来の Layer 2/3 Switchと異なり、より柔軟なパケットマッチング・変換・転送処理を実現するため、従来のネットワーク・プロセッサとは異なる設計が必要となります。
本発表では、既存のネットワーク・プロセッサではなく、FPGAを用いた独自設計によるOpenFlow Switchの開発経験を元に、ハードウェア処理にかかわるOpenFlowの機能概要と、FPGAを用いた実現方法や課題について解説します。
Announcing Amazon EC2 F1 Instances with Custom FPGAs
Amazon EC2 F1 is a new compute instance with programmable hardware for application acceleration. With F1, you can directly access custom FPGA hardware on the instance in a few clicks.
Learning Objectives:
• Learn about the capabilities, features, and benefits of the new F1 instances
• Develop your FPGA using the F1 Hardware Developer Kit and FPGA Developer AMI
• Deploy your FPGA acceleration code using F1 instances
• Use F1 instances for hardware acceleration in your applications
• Learn how to offer pre-packaged Amazon FPGA Machine Images (AFIs) to your customers through the AWS Marketplace
Tensor flow usergroup 2016 (公開版)
日本Googleオフィスで開催されたTensorFlow User Groupでしゃべったときのスライドです。TensorFlowからFPGAに落とす方法と2値化ディープニューラルネットワークについて。
The Outcome Economy
1) The document discusses the opportunity for technology to improve organizational efficiency and transition economies into a "smart and clean world."
2) It argues that aggregate efficiency has stalled at around 22% for 30 years due to limitations of the Second Industrial Revolution, but that digitizing transport, energy, and communication through technologies like blockchain can help manage resources and increase efficiency.
3) Technologies like precision agriculture, cloud computing, robotics, and autonomous vehicles may allow for "dematerialization" and do more with fewer physical resources through effects like reduced waste and need for transportation/logistics infrastructure.
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Machine Learning with New Hardware Challegens
Describe basic neural network design and focus on Convolutional Neural Network architecture. Explain why CPU and GPU can't fulfill CNN hardware requirement. List out three hardware examples: Nvidia, Microsoft and Google. Finally highlight optimization approach for CNN design.
"Trade-offs in Implementing Deep Neural Networks on FPGAs," a Presentation fr...
"Trade-offs in Implementing Deep Neural Networks on FPGAs," a Presentation fr...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them."Accelerating Deep Learning Using Altera FPGAs," a Presentation from Intel
"Accelerating Deep Learning Using Altera FPGAs," a Presentation from IntelEdge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Bill Jenkins, Senior Product Specialist for High Level Design Tools at Intel, presents the "Accelerating Deep Learning Using Altera FPGAs" tutorial at the May 2016 Embedded Vision Summit.
While large strides have recently been made in the development of high-performance systems for neural networks based on multi-core technology, significant challenges in power, cost and, performance scaling remain. Field-programmable gate arrays (FPGAs) are a natural choice for implementing neural networks because they can combine computing, logic, and memory resources in a single device. Intel's Programmable Solutions Group has developed a scalable convolutional neural network reference design for deep learning systems using the OpenCL programming language built with our SDK for OpenCL. The design performance is being benchmarked using several popular CNN benchmarks: CIFAR-10, ImageNet and KITTI.
Building the CNN with OpenCL kernels allows true scaling of the design from smaller to larger devices and from one device generation to the next. New designs can be sized using different numbers of kernels at each layer. Performance scaling from one generation to the next also benefits from architectural advancements, such as floating-point engines and frequency scaling. Thus, you achieve greater than linear performance and performance per watt scaling with each new series of devices.FPGAで作るOpenFlow Switch （FPGAエクストリーム・コンピューティング 第6回） FPGAX#6
OpenFlow Switchは従来の Layer 2/3 Switchと異なり、より柔軟なパケットマッチング・変換・転送処理を実現するため、従来のネットワーク・プロセッサとは異なる設計が必要となります。
本発表では、既存のネットワーク・プロセッサではなく、FPGAを用いた独自設計によるOpenFlow Switchの開発経験を元に、ハードウェア処理にかかわるOpenFlowの機能概要と、FPGAを用いた実現方法や課題について解説します。
Announcing Amazon EC2 F1 Instances with Custom FPGAs
Amazon EC2 F1 is a new compute instance with programmable hardware for application acceleration. With F1, you can directly access custom FPGA hardware on the instance in a few clicks.
Learning Objectives:
• Learn about the capabilities, features, and benefits of the new F1 instances
• Develop your FPGA using the F1 Hardware Developer Kit and FPGA Developer AMI
• Deploy your FPGA acceleration code using F1 instances
• Use F1 instances for hardware acceleration in your applications
• Learn how to offer pre-packaged Amazon FPGA Machine Images (AFIs) to your customers through the AWS Marketplace
Tensor flow usergroup 2016 (公開版)
日本Googleオフィスで開催されたTensorFlow User Groupでしゃべったときのスライドです。TensorFlowからFPGAに落とす方法と2値化ディープニューラルネットワークについて。
The Outcome Economy
1) The document discusses the opportunity for technology to improve organizational efficiency and transition economies into a "smart and clean world."
2) It argues that aggregate efficiency has stalled at around 22% for 30 years due to limitations of the Second Industrial Revolution, but that digitizing transport, energy, and communication through technologies like blockchain can help manage resources and increase efficiency.
3) Technologies like precision agriculture, cloud computing, robotics, and autonomous vehicles may allow for "dematerialization" and do more with fewer physical resources through effects like reduced waste and need for transportation/logistics infrastructure.
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"Trade-offs in Implementing Deep Neural Networks on FPGAs," a Presentation fr...
"Trade-offs in Implementing Deep Neural Networks on FPGAs," a Presentation fr...
"Accelerating Deep Learning Using Altera FPGAs," a Presentation from Intel
"Accelerating Deep Learning Using Altera FPGAs," a Presentation from Intel
FPGAで作るOpenFlow Switch （FPGAエクストリーム・コンピューティング 第6回） FPGAX#6
FPGAで作るOpenFlow Switch （FPGAエクストリーム・コンピューティング 第6回） FPGAX#6
Announcing Amazon EC2 F1 Instances with Custom FPGAs
Announcing Amazon EC2 F1 Instances with Custom FPGAs
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- 1. - 1 - DSD 2분반 제출일 : 2011.11.13 Dicegame 구현하기 - ver.verilog - 20800577 장 호 상
- 2. - 2 - 1. 소스코드 ① dice_top.v ② datapath.v fd4re.v - 4bit D flip-flop with enable&sync reset
- 3. - 3 - ③ dice.v - 주사위 값을 결정해주는 코드
- 4. - 4 - ④ dice_comp.v - 5, 7, 11, point 와 sum 비교 ⑤ led_out.v - 이겼을 때와 졌을 때, 존슨카운터를 통해 led로 출력해주는 소스 ⑥ cj4re.v -4bit 존슨카운터 D-flip flop with enable & sync reset
- 5. - 5 - ⑦ ss_drive.v - 4bit decimal number를 7-segment display로 출력해주는 소스
- 6. - 6 - ⑧ ss_decoder.v
- 7. - 7 - ⑨ clk_div.v - 클락 분주기 ⑩ pusle.v - debouce single pulse 생성기 D-flip flop with en & async reset
- 8. - 8 - 2. 테스트벤치 ① test_pulse.v - debounce single pulse 생성기 시뮬레이션
- 9. - 9 - ② test_core.v - fsm과 datapath만 이용한 시뮬레이션
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