1. An FPGA-based Scalable Simulation Accelerator called ScalableCore is presented for simulating Tile architectures like the M-Core manycore processor.
2. ScalableCore partitions the target processor across multiple FPGAs, with each FPGA representing a "ScalableCore Unit" containing part of the processor. Units are connected via a "ScalableCore Board" to simulate the entire processor faster.
3. An initial ScalableCore system was implemented to simulate the M-Core manycore processor with up to 64 cores distributed across 64 ScalableCore Units/FPGAs. This allows simulation speed to scale with the number of FPGAs used.
ScalableCore System: A Scalable Many-core Simulator by Employing Over 100 FPGAsShinya Takamaeda-Y
The document summarizes a presentation about the ScalableCore System, a scalable many-core simulator that employs over 100 FPGAs. It maps a target many-core processor across multiple FPGA boards, each simulating a tile/core. This allows achieving scalable simulation speeds as the number of target cores increases. Evaluation shows the resource usage and faster simulation speeds compared to software simulators as the number of simulated nodes increases from 16 to 100.
Cameron Swen is the Divisional Marketing Manager for AMD’s Embedded Solutions Division. He is responsible for outbound marketing and works with AMDs customers to develop and market board and system level solutions to serve the COTS market.
High Performance Computing Infrastructure: Past, Present, and Futurekarl.barnes
This document discusses high performance computing infrastructure from the past to present and future. It begins with an introduction to reconfigurable computing and describes the Bison Configurable Digital Signal Processor and its design flow. It discusses function cores and modules that have been developed. It also describes a remote reconfigurable computer called RARE and a parallel and configurable computer system. Finally, it discusses high performance weather forecast modeling and a proposed reconfigurable and open architecture module for unmanned systems.
Presentation at FreedomHEC 2012 Conference. 0xlab extends DMTCP (Distributed Multi-Threaded CheckPointing) to enable Android checkpointing, which leads to resume to stored state for faster Android boot time and make better product field trial experience.
This document provides an introduction to GPU computing. It discusses the architectural differences between CPUs and GPUs and when each is better suited for certain tasks. It also overview several GPU programming models such as CUDA, OpenCL, and directives. Finally, it discusses approaches for analyzing GPU performance, including using explicit events, the CUDA profiler, and CrayPAT tools.
The document discusses implementing checkpointing for Android to speed up boot time and development process. It proposes checkpointing processes to save their state, then restore that state to resume execution faster after crashes or reboots. This would allow resuming to a stored state for a faster Android boot. Challenges include checkpointing the network stack state and sockets. Existing checkpointing mechanisms like CryoPID and BLCR are mentioned. DMTCP is discussed as it supports checkpointing applications without modifications in userspace.
This document describes a test architecture that separates parallel program communication from computation kernels to enable future partial dynamic reconfiguration of processing elements (PEs) on FPGAs. The architecture implements static softcore processors as test PEs on a Xilinx Virtex 5 FPGA. One PE acts as a host cell running MPI for communication, while other PEs act as computing cells running computation kernels. The NAS Parallel Benchmarks integer sort is used to benchmark communication and computation performance on this architecture.
ScalableCore System: A Scalable Many-core Simulator by Employing Over 100 FPGAsShinya Takamaeda-Y
The document summarizes a presentation about the ScalableCore System, a scalable many-core simulator that employs over 100 FPGAs. It maps a target many-core processor across multiple FPGA boards, each simulating a tile/core. This allows achieving scalable simulation speeds as the number of target cores increases. Evaluation shows the resource usage and faster simulation speeds compared to software simulators as the number of simulated nodes increases from 16 to 100.
Cameron Swen is the Divisional Marketing Manager for AMD’s Embedded Solutions Division. He is responsible for outbound marketing and works with AMDs customers to develop and market board and system level solutions to serve the COTS market.
High Performance Computing Infrastructure: Past, Present, and Futurekarl.barnes
This document discusses high performance computing infrastructure from the past to present and future. It begins with an introduction to reconfigurable computing and describes the Bison Configurable Digital Signal Processor and its design flow. It discusses function cores and modules that have been developed. It also describes a remote reconfigurable computer called RARE and a parallel and configurable computer system. Finally, it discusses high performance weather forecast modeling and a proposed reconfigurable and open architecture module for unmanned systems.
Presentation at FreedomHEC 2012 Conference. 0xlab extends DMTCP (Distributed Multi-Threaded CheckPointing) to enable Android checkpointing, which leads to resume to stored state for faster Android boot time and make better product field trial experience.
This document provides an introduction to GPU computing. It discusses the architectural differences between CPUs and GPUs and when each is better suited for certain tasks. It also overview several GPU programming models such as CUDA, OpenCL, and directives. Finally, it discusses approaches for analyzing GPU performance, including using explicit events, the CUDA profiler, and CrayPAT tools.
The document discusses implementing checkpointing for Android to speed up boot time and development process. It proposes checkpointing processes to save their state, then restore that state to resume execution faster after crashes or reboots. This would allow resuming to a stored state for a faster Android boot. Challenges include checkpointing the network stack state and sockets. Existing checkpointing mechanisms like CryoPID and BLCR are mentioned. DMTCP is discussed as it supports checkpointing applications without modifications in userspace.
This document describes a test architecture that separates parallel program communication from computation kernels to enable future partial dynamic reconfiguration of processing elements (PEs) on FPGAs. The architecture implements static softcore processors as test PEs on a Xilinx Virtex 5 FPGA. One PE acts as a host cell running MPI for communication, while other PEs act as computing cells running computation kernels. The NAS Parallel Benchmarks integer sort is used to benchmark communication and computation performance on this architecture.
This document discusses delay tolerant streaming services for transmitting live video from mobile devices over unstable mobile ad-hoc networks. It motivates the need for such services when conventional network infrastructure is unavailable. The approach involves building an adaptive overlay network on top of the mobile ad-hoc network to enable delayed and disrupted video streaming. Several technical challenges are outlined and initial results are highlighted from experiments and simulations evaluating the feasibility of video streaming over mobile ad-hoc networks formed by mobile phones. Future work is discussed around developing a prototype system and exploring fundamental changes needed to support emerging applications and technologies.
Toward a practical “HPC Cloud”: Performance tuning of a virtualized HPC clusterRyousei Takano
1) Performance tuning methods for HPC Cloud include PCI passthrough, NUMA affinity, and reducing VMM noise to improve performance and close the gap with bare metal machines.
2) Evaluation of MPI and HPC applications on a 16-node cluster showed PCI passthrough improved MPI bandwidth close to bare metal, and NUMA affinity improved performance up to 2%.
3) Parallel efficiency of coarse-grained applications was comparable to bare metal, but fine-grained applications saw up to 22% degradation due to communication overhead and virtualization.
Xvisor is an open source lightweight hypervisor for ARM architectures. It uses a technique called cpatch to modify guest operating system binaries, replacing privileged instructions with hypercalls. This allows the guest OS to run without privileges in user mode under the hypervisor. Xvisor also implements virtual CPU and memory management to isolate guest instances and virtualize physical resources for multiple operating systems.
GPUs are specialized processors designed for graphics processing. CUDA (Compute Unified Device Architecture) allows general purpose programming on NVIDIA GPUs. CUDA programs launch kernels across a grid of blocks, with each block containing multiple threads that can cooperate. Threads have unique IDs and can access different memory types including shared, global, and constant memory. Applications that map well to this architecture include physics simulations, image processing, and other data-parallel workloads. The future of CUDA includes more general purpose uses through GPGPU and improvements in virtual memory, size, and cooling.
This document discusses modifications made to the Xen code to create Xenon, a high-assurance version of Xen. It describes simplifying and refactoring the code based on complexity metrics and modularity guidelines. Construction guidelines for Xenon include adding comments, pseudocode design language files, readme files, formatting tools, and limits on complexity, abstraction, and coding practices. The goal is to develop a separation hypervisor with an evidence package for high assurance.
Toward a practical “HPC Cloud”: Performance tuning of a virtualized HPC clusterRyousei Takano
This document evaluates the performance of a virtualized HPC cluster using the HPC Challenge benchmark suite. It investigates three performance tuning techniques: PCI passthrough to bypass virtualization overhead for the network interface card, NUMA affinity to improve memory access performance, and reducing "VMM noise" like unnecessary services on the host OS. The results show these techniques can improve performance of the virtualized cluster to be close to that of a non-virtualized or "bare metal" system, realizing a more practical "true HPC Cloud."
This document proposes a thread clustering technique for sharing-aware thread scheduling on multiprocessor systems. It detects sharing patterns between threads using hardware performance counters and samples of remote cache accesses. Threads are clustered based on their sharing signatures to improve data locality and reduce cross-chip traffic. Experimental results show the approach reduces remote cache accesses by up to 70% and improves performance up to 7% across several workloads.
[Harvard CS264] 05 - Advanced-level CUDA Programmingnpinto
The document discusses optimizations for memory and communication in massively parallel computing. It recommends caching data in faster shared memory to reduce loads and stores to global device memory. This can improve performance by avoiding non-coalesced global memory accesses. The document provides an example of coalescing writes for a matrix transpose by first loading data into shared memory and then writing columns of the tile to global memory in contiguous addresses.
[Harvard CS264] 11a - Programming the Memory Hierarchy with Sequoia (Mike Bau...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
[Harvard CS264] 11b - Analysis-Driven Performance Optimization with CUDA (Cli...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
PEER 1 Offers NVIDIA GPU to Accelerate High Performance Applications
PEER 1 has teamed up with NVIDIA the creator of the GPU and a world leader in visual computing, to provide high performance GPU Cloud applications. NVIDIA’s GPUs are well known for making customer software run faster and PEER 1 is offering a number of services that run on NVIDA’s GPUs. PEER 1’s cloud service is built on NVIDIA Telsa GPU’s delivering supercomputing performance in the cloud to solve much tougher problems. Click here to find out how PEER 1 and NVIDIA can transform your business.
Algorithmic Memory Increases Memory Performance by an Order of Magnitudechiportal
Algorithmic memory increases memory performance by an order of magnitude using algorithms and memory macros. It presents a standard memory interface while adding no clock cycle latency. This allows creating multiport functionality from single-port physical memory. Algorithmic memory lowers area and power while increasing available memory ports and clock performance compared to physical memory alone. It provides configurable high performance, density-efficient, and power-efficient memories to alleviate the growing processor-embedded memory performance gap.
This presentation covers the working model about Process, Thread, system call, Memory operations, Binder IPC, and interactions with Android frameworks.
Dan Schatzberg, Jonathan Appavoo, Orran Krieger, and Eric Van Hensbergen. Scalable elastic systems architecture. In Proceedings of the ASPLOS Runtime Environment/Systems, Layering, and Virtualized
Environments (RESoLVE) Workshop. ASPLOS, March 2011.
DFX Architecture for High-performance Multi-core MicroprocessorsIshwar Parulkar
This presentation was given at ITC 2008 (International Test Conference). It deals with DFX challenges and solution for high count multi-core microprocessors. Acknowledgment: Co-authors on ITC presentation - Gaurav Agarwal, Sriram Anandakumar, Gordon Liu, Rajesh Pendurkar, Krishna Rajan and Frank Chiu.
This document summarizes an MIT lecture on GPU cluster programming using MPI. It provides administrative details such as homework due dates and project information. It also announces various donations of computing resources for the class, including Amazon AWS credits and a Tesla graphics card for the best project. The lecture outline covers the problem of computations too large for a single CPU, an introduction to MPI, MPI basics, using MPI with CUDA, and other parallel programming approaches.
A CGRA-based Approachfor Accelerating Convolutional Neural NetworksShinya Takamaeda-Y
The document presents an approach for accelerating convolutional neural networks (CNNs) using a coarse-grained reconfigurable array (CGRA) called EMAX. EMAX features processing elements with local memory to improve data locality and memory bandwidth utilization. CNN computations like convolutions are mapped to EMAX by assigning weight matrices to constant registers and performing numerous small matrix multiplications in parallel. Evaluation shows EMAX achieves better performance per memory bandwidth and area than GPUs for CNN workloads due to its optimization for small matrix operations.
This document discusses delay tolerant streaming services for transmitting live video from mobile devices over unstable mobile ad-hoc networks. It motivates the need for such services when conventional network infrastructure is unavailable. The approach involves building an adaptive overlay network on top of the mobile ad-hoc network to enable delayed and disrupted video streaming. Several technical challenges are outlined and initial results are highlighted from experiments and simulations evaluating the feasibility of video streaming over mobile ad-hoc networks formed by mobile phones. Future work is discussed around developing a prototype system and exploring fundamental changes needed to support emerging applications and technologies.
Toward a practical “HPC Cloud”: Performance tuning of a virtualized HPC clusterRyousei Takano
1) Performance tuning methods for HPC Cloud include PCI passthrough, NUMA affinity, and reducing VMM noise to improve performance and close the gap with bare metal machines.
2) Evaluation of MPI and HPC applications on a 16-node cluster showed PCI passthrough improved MPI bandwidth close to bare metal, and NUMA affinity improved performance up to 2%.
3) Parallel efficiency of coarse-grained applications was comparable to bare metal, but fine-grained applications saw up to 22% degradation due to communication overhead and virtualization.
Xvisor is an open source lightweight hypervisor for ARM architectures. It uses a technique called cpatch to modify guest operating system binaries, replacing privileged instructions with hypercalls. This allows the guest OS to run without privileges in user mode under the hypervisor. Xvisor also implements virtual CPU and memory management to isolate guest instances and virtualize physical resources for multiple operating systems.
GPUs are specialized processors designed for graphics processing. CUDA (Compute Unified Device Architecture) allows general purpose programming on NVIDIA GPUs. CUDA programs launch kernels across a grid of blocks, with each block containing multiple threads that can cooperate. Threads have unique IDs and can access different memory types including shared, global, and constant memory. Applications that map well to this architecture include physics simulations, image processing, and other data-parallel workloads. The future of CUDA includes more general purpose uses through GPGPU and improvements in virtual memory, size, and cooling.
This document discusses modifications made to the Xen code to create Xenon, a high-assurance version of Xen. It describes simplifying and refactoring the code based on complexity metrics and modularity guidelines. Construction guidelines for Xenon include adding comments, pseudocode design language files, readme files, formatting tools, and limits on complexity, abstraction, and coding practices. The goal is to develop a separation hypervisor with an evidence package for high assurance.
Toward a practical “HPC Cloud”: Performance tuning of a virtualized HPC clusterRyousei Takano
This document evaluates the performance of a virtualized HPC cluster using the HPC Challenge benchmark suite. It investigates three performance tuning techniques: PCI passthrough to bypass virtualization overhead for the network interface card, NUMA affinity to improve memory access performance, and reducing "VMM noise" like unnecessary services on the host OS. The results show these techniques can improve performance of the virtualized cluster to be close to that of a non-virtualized or "bare metal" system, realizing a more practical "true HPC Cloud."
This document proposes a thread clustering technique for sharing-aware thread scheduling on multiprocessor systems. It detects sharing patterns between threads using hardware performance counters and samples of remote cache accesses. Threads are clustered based on their sharing signatures to improve data locality and reduce cross-chip traffic. Experimental results show the approach reduces remote cache accesses by up to 70% and improves performance up to 7% across several workloads.
[Harvard CS264] 05 - Advanced-level CUDA Programmingnpinto
The document discusses optimizations for memory and communication in massively parallel computing. It recommends caching data in faster shared memory to reduce loads and stores to global device memory. This can improve performance by avoiding non-coalesced global memory accesses. The document provides an example of coalescing writes for a matrix transpose by first loading data into shared memory and then writing columns of the tile to global memory in contiguous addresses.
[Harvard CS264] 11a - Programming the Memory Hierarchy with Sequoia (Mike Bau...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
[Harvard CS264] 11b - Analysis-Driven Performance Optimization with CUDA (Cli...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
PEER 1 Offers NVIDIA GPU to Accelerate High Performance Applications
PEER 1 has teamed up with NVIDIA the creator of the GPU and a world leader in visual computing, to provide high performance GPU Cloud applications. NVIDIA’s GPUs are well known for making customer software run faster and PEER 1 is offering a number of services that run on NVIDA’s GPUs. PEER 1’s cloud service is built on NVIDIA Telsa GPU’s delivering supercomputing performance in the cloud to solve much tougher problems. Click here to find out how PEER 1 and NVIDIA can transform your business.
Algorithmic Memory Increases Memory Performance by an Order of Magnitudechiportal
Algorithmic memory increases memory performance by an order of magnitude using algorithms and memory macros. It presents a standard memory interface while adding no clock cycle latency. This allows creating multiport functionality from single-port physical memory. Algorithmic memory lowers area and power while increasing available memory ports and clock performance compared to physical memory alone. It provides configurable high performance, density-efficient, and power-efficient memories to alleviate the growing processor-embedded memory performance gap.
This presentation covers the working model about Process, Thread, system call, Memory operations, Binder IPC, and interactions with Android frameworks.
Dan Schatzberg, Jonathan Appavoo, Orran Krieger, and Eric Van Hensbergen. Scalable elastic systems architecture. In Proceedings of the ASPLOS Runtime Environment/Systems, Layering, and Virtualized
Environments (RESoLVE) Workshop. ASPLOS, March 2011.
DFX Architecture for High-performance Multi-core MicroprocessorsIshwar Parulkar
This presentation was given at ITC 2008 (International Test Conference). It deals with DFX challenges and solution for high count multi-core microprocessors. Acknowledgment: Co-authors on ITC presentation - Gaurav Agarwal, Sriram Anandakumar, Gordon Liu, Rajesh Pendurkar, Krishna Rajan and Frank Chiu.
This document summarizes an MIT lecture on GPU cluster programming using MPI. It provides administrative details such as homework due dates and project information. It also announces various donations of computing resources for the class, including Amazon AWS credits and a Tesla graphics card for the best project. The lecture outline covers the problem of computations too large for a single CPU, an introduction to MPI, MPI basics, using MPI with CUDA, and other parallel programming approaches.
A CGRA-based Approachfor Accelerating Convolutional Neural NetworksShinya Takamaeda-Y
The document presents an approach for accelerating convolutional neural networks (CNNs) using a coarse-grained reconfigurable array (CGRA) called EMAX. EMAX features processing elements with local memory to improve data locality and memory bandwidth utilization. CNN computations like convolutions are mapped to EMAX by assigning weight matrices to constant registers and performing numerous small matrix multiplications in parallel. Evaluation shows EMAX achieves better performance per memory bandwidth and area than GPUs for CNN workloads due to its optimization for small matrix operations.
This document provides information about using high-level programming languages to generate hardware implementations on FPGAs. It discusses how high-level synthesis (HLS) can be used to synthesize register transfer level (RTL) descriptions from C/C++ or Python code. This allows hardware to be programmed at a higher level of abstraction without having to manually write RTL code. Specific HLS tools mentioned include Xilinx Vivado HLS, Altera OpenCL, Veriloggen for Python, and synthesizing hardware from languages like C, C++, Java, and Python.
Debian Linux on Zynq (Xilinx ARM-SoC FPGA) Setup Flow (Vivado 2015.4)Shinya Takamaeda-Y
The document describes the process to set up Debian Linux on a Zynq FPGA board using a Zybo board as a reference platform. The key steps include:
1. Developing the hardware design in Vivado, including adding a CPU, GPIO for LEDs and switches, and generating a bitstream;
2. Compiling U-boot and the Linux kernel, as well as creating a device tree and root filesystem;
3. Setting up an SD card and booting the system from the SD card.
This document provides an overview of system on chip (SoC) design. It discusses that a SoC integrates all components of an electronic system onto a single chip, including digital, analog and radio frequency functions. The SoC design process involves identifying user needs and integrating various intellectual property blocks. It describes the SoC design flow, fundamentals like using soft and hard IP cores, and considerations like architecture strategy and validation. Key aspects covered include SoC architecture, on-chip buses to connect IP cores, and examples of commercial SoCs.
(1) An FPGA is a field-programmable gate array that contains configurable digital components that can be interconnected by the user. (2) The Advanced Digital Technologies student group uses FPGAs for projects such as a datalogger and the X-ISCKER embedded processor design. (3) X-ISCKER is an open source FPGA-based embedded processor project that aims to teach computer architectures through implementing RISC and CISC processors on an FPGA.
This document provides an overview of FPGA technology. It describes that an FPGA is a field programmable gate array that can be reprogrammed after manufacturing. The core components of an FPGA include look-up tables, flip-flops, multiplexors, I/O blocks, programmable interconnects, and SRAM memory cells. FPGAs offer advantages over ASICs like quick time to market and reprogrammability. Major FPGA manufacturers like Xilinx and Altera integrate additional components into their devices like RAM blocks, DSP blocks, and embedded processor cores.
This document provides an overview of system on chip (SoC) design. It discusses that a SoC integrates all components of an electronic system onto a single chip, and that SoC design involves identifying user needs and integrating various intellectual property blocks. The document then covers SoC fundamentals like the use of soft and hard IP cores, the design flow from specification to fabrication, and strategies for addressing SoC complexity through partitioning, abstraction levels, and reuse of pre-designed components.
The presentation provides an introduction to the emulation world, in particular to the mythical Commodore 64 and its peripherals, like disk drive, printer, cartridges. To truly emulate the software written for this 8-bit home computer it is mandatory to be much accurate as possible and reproduce every single aspect of the real machine, starting from the chips that compose the hardware architecture. Beside the emulation topics the presentation faces some Scala performance issues that come up when you have to optimize low level operations. At the end I'll show you a demo where we'll see the emulator running a game and a demo-scene, one of the hardest software to emulate.
This document discusses image processing applications using Vivado for FPGAs. It provides information on FPGA architecture including distributed memory, block RAM features, and core generator. An example of a real-time breast cancer diagnosis application using YOLO on an FPGA board is described. A second example discusses implementing CCSDS standard DWT-based hyperspectral image decompression on an FPGA using techniques like Haar wavelet transform and MAP encoding.
The document summarizes a new type of smart camera called the PC Camera. The PC Camera integrates a fully functional high-performance industrial PC inside the camera. This allows for zero CPU overhead on image data delivery and a true zero copy paradigm. The PC Camera uses an AMD accelerated processing unit (APU) which collocates a CPU and GPU on a single die. This provides very high computational performance of over 90 GFlops in a small form factor while avoiding the limitations of traditional smart cameras.
Altreonic was spun off in 2008 from Eonic Systems to focus on real-time operating systems using formal techniques. Their OpenComRTOS is a small, network-centric real-time OS that uses CSP concurrency and can scale from 1 to over 10,000 nodes. It provides priority-based communication and fault tolerance and has been implemented on many heterogeneous platforms from DSPs to many-core systems.
The document discusses AMD's Barcelona quad core microprocessor. It provides details on the Barcelona architecture including its quad core die layout with two cores per module and shared L3 cache. It also examines AMD's 65nm transistor structure and SRAM cache design. Performance comparisons are made between AMD's native Barcelona quad core and Intel's quad core solution using two dual core dies. Key advantages and challenges for both AMD and Intel's quad core approaches are identified.
A Dataflow Processing Chip for Training Deep Neural Networksinside-BigData.com
In this deck from the Hot Chips conference, Chris Nicol from Wave Computing presents: A Dataflow Processing Chip for Training Deep Neural Networks.
Watch the video: https://wp.me/p3RLHQ-k6W
Learn more: https://wavecomp.ai/
and
http://www.hotchips.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Krupesh Patel has over 5 years of experience designing and developing FPGA IP cores. He has experience with SD host controllers, NAND flash memory controllers, microcontrollers, and error correction coding. Currently he is working on the design of an SD UHS-II host controller IP core. Previously he has designed IP cores compliant with ONFI, SD, and eMMC specifications.
Mirabilis_Design AMD Versal System-Level IP LibraryDeepak Shankar
Mirabilis Design provides the VisualSim Versal Library that enable System Architect and Algorithm Designers to quickly map the signal processing algorithms onto the Versal FPGA and define the Fabric based on the performance. The Versal IP support all the heterogeneous resource.
This document summarizes a presentation on reverse engineering the Rocket-Chip SoC generator to develop a customized SoC called Aghaaz. The presentation covers deconstructing the Rocket-Chip software architecture, developing a Micro-Architecture and Software Specification (MASS) document, configuring an Aghaaz SoC using the MASS document, and generating the SoC from the Rocket-Chip generator. Key aspects included developing object-oriented representations of Rocket-Chip modules, flowcharts to explain the code, and configuring an RV32 core with caches and extensions.
Various processor architectures are described in this presentation. It could be useful for people working for h/w selection and processor identification.
FPGA_prototyping proccesing with conclusionPersiPersi1
This document discusses FPGA prototyping and system on chip (SoC) design using the Xilinx Zynq architecture. It begins with an overview of FPGA prototyping benefits like architecture exploration, software development and validation. Next, it describes the basic elements of a typical SoC like processors, memory and peripherals. It then introduces the Zynq architecture which combines an ARM processor with programmable logic on a single chip. Key aspects of the Zynq such as the processing system, application processing unit, external interfaces and programmable logic resources are explained. Memory mapped and FIFO interfaces for hardware/software communication are also covered. Finally, the basic design flow for Zynq SoC
Industrial trends in heterogeneous and esoteric computePerry Lea
This document discusses several emerging computing architectures including The Machine, computational memory, computational RAM, managed language accelerators, and neuromorphic engines. For each architecture, it outlines the key technical claims and challenges, and provides a prediction on the technology's likelihood of widespread adoption and penetration into markets like mobile, embedded, and HPC. Overall, the document analyzes these novel approaches against the realities of technology maturation, programming difficulties, application limitations, customer acceptance, and commercial viability.
This document discusses streaming SIMD extensions (SSE) and how to use SIMD instructions to boost program performance. It defines SSE as a set of CPU instructions for applications like signal processing that use single instruction, multiple data (SIMD) parallelism. The document outlines what SSE is, the advantages of SIMD, how to identify if an application can benefit from SSE, different SSE versions, coding methods like assembly and intrinsics, and references for further information.
Ajay Kumar Bandaru is a senior layout design engineer with over 5 years of experience working on memory designs from 110nm to 14nm nodes. He has expertise in SRAM, register file, and ROM layouts as well as backend verification. Some of his responsibilities include floorplanning, placement, developing compiler-compatible core arrays, and leaf cell and instance-level design rule and layout versus schematic checks. He is proficient with EDA tools from Cadence and Synopsys and has worked on projects for various foundries and clients.
TitanIC presented, "ODSA Use Case - SmartNIC," at the ODSA Workshop. The charter of the ODSA (Open Domain Specification Architecture) Workgroup is to define an open specification that enables building of Domain Specific Accelerator silicon using best-of-breed components from the industry made available as chiplet dies that can be integrated together as Lego blocks on an organic substrate packaging layer. The resulting multi-chip module (MCM) silicon can be produced at significantly lower development and manufacturing costs, and will deliver much needed performance per watt and performance per dollar efficiencies in networking, security, machine learning and other applications. The ODSA Workgroup also intends to deliver implementations of the specification as board-level prototypes, RTL code and libraries.
This document discusses hardware trends and challenges for building exascale computers. It describes the evolution of processor/node architectures including multi-core and many-core designs. Reaching exascale performance will require addressing power consumption, concurrency, scalability, and fault tolerance issues. Evolutionary paths using commodity processors are unlikely to succeed, while aggressive approaches using clean-sheet designs for low-power customized chips may be needed to achieve exascale performance by 2018. International efforts are underway to develop exascale systems, but overcoming technical challenges to efficiently utilize extreme parallelism remains difficult.
Similar to An FPGA-based Scalable Simulation Accelerator for Tile Architectures @HEART2011 (20)
This document discusses NNgen, a tool for generating hardware implementations of neural networks from high-level models. It can generate optimized RTL and IP-XACT from models defined using frameworks like TensorFlow or ONNX. NNgen uses the Veriloggen library for hardware synthesis from Python, generating FSMs and scheduled pipelines to implement DNN layers as hardware accelerators. It aims to bridge the gap between deep learning and hardware for deploying neural networks in embedded systems.
This document discusses NNgen, a tool for generating neural network hardware implementations from TensorFlow models. NNgen takes a TensorFlow model as input, performs optimizations, and generates an FPGA implementation including a control unit, computing units, RAM blocks, and interconnects. It outputs RTL code and an IP-XACT description of the generated neural network hardware accelerator. Diagrams show an example convolutional layer implementation generated by NNgen, including weight and activation memory blocks, multiply-accumulate units, addition trees, and reuse of computation units via a substream pool.
This document discusses Veriloggen, a Python framework for generating Verilog HDL code from Python. It allows designing hardware at the register-transfer level using Python by mapping Python constructs to Verilog modules, always blocks, wires, and other Verilog constructs. Veriloggen includes modules for RTL generation (Core), connecting Python threads to finite state machines (Thread), and defining streaming hardware (Stream). It aims to support a "Veriloggen for DSL X" approach to create domain-specific hardware description languages in Python.
Veriloggen is a Python library that allows users to generate RTL from Python code for FPGA implementation. It supports threads to model hardware tasks, streams to connect hardware components, and intrinsic functions that map to RTL. The library can synthesize Python code into Verilog for FPGA synthesis and implementation, providing an easier high-level approach to developing FPGA hardware compared to writing RTL directly in Verilog.
Pythonによるカスタム可能な高位設計技術 (Design Solution Forum 2016@新横浜)Shinya Takamaeda-Y
Veriloggen is a Python library that allows users to generate Verilog HDL code from Python. It provides objects and methods to define RTL modules in Python, including module inputs/outputs, registers, assignments, always blocks, etc. When the Veriloggen object is passed to the to_verilog() method, it traverses the object and generates equivalent Verilog HDL code. This allows rapid prototyping of RTL designs in Python without having to write low-level Verilog code directly.
The document discusses Twitter and GitHub accounts, an IPSJ conference, and hardware including an Intel Core i7, FPGA boards from Digilent and ScalableCore, and code snippets for C programs and hardware designs including for a convolutional neural network layer.
A Framework for Efficient Rapid Prototyping by Virtually Enlarging FPGA Resou...Shinya Takamaeda-Y
A Framework for Efficient Rapid Prototyping by Virtually Enlarging FPGA Resources (ReConFig2014@Cancun, Mexico)
flipSyrup, a new framework for rapid prototyping is proposed.
PyCoRAM: Yet Another Implementation of CoRAM Memory Architecture for Modern F...Shinya Takamaeda-Y
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An FPGA-based Scalable Simulation Accelerator for Tile Architectures @HEART2011
1. 14:30 – 15:00 June 2, 2011
HEART 2011 @Imperial College London
An FPGA-based Scalable Simulation
Accelerator for Tile Architectures
Shinya Takamaeda-Yamazaki†‡, Ryosuke Sasakawa†,
Yoshito Sakaguchi†, Kenji Kise†
†Tokyo Institute of Technology, Japan
‡JSPS Research Fellow
2. This presentation shows ScalableCore system
n Multi-FPGA system for Tile architecture simulations
l Achieving SCALABLE simulation speed
Target Core
System
Function
3. Agenda
n Background & Motivation
n Proposal: ScalableCore
n System Implementation
l Overall system
l Components: ScalableCore Unit & Board
l Logic Hierarch & Architecture
n Evaluation
l Simulation Speed
l Power
n Conclusion
4. Background: Multicores to Many-cores
Intel Single Chip Cloud Computer
48 cores (x86)
TILERA TILE-Gx100
100 cores (MIPS)
5. Simulation Target Manycore: M-Core
n Tile architecture with 2D mesh network
l A Node has: Core, Local Memory, INCC (DMA controller) and Router
l Local Memory: Independent Address Space, Data transfer by DMAs
DRAM Controller DRAM Controller
Local
Memory
Core
INCC
R
Node
DRAM Controller DRAM Controller
6. How to evaluate the architectures?
n Customizability vs. Simulation Speed
l We want to run a large benchmark fast
Reality
Chip
Easy construction of
ideal system without
HW limitations FPGA
Real but
Simulator
expensive
Software
Faster simulation and
Simulator
customizable
Difficulty to construct
7. Less scalability of simulation speed
on software simulators
n Decreasing speed with the increasing # target cores
l SimMc :M-Core simulator
l Difficult to achieve the scalable speed
• Overhead for cycle accurate simulation
400
343 Speed degradation
350
more than the increasing # cores
Simulation Speed
300
[K cycle / sec]
250
200
149
150
96
100 70
50
0
16 32 48 64
# Target Cores
Simulation Speed on SimMc (M-Core simulator)
8. Motivation
n Achieve the SCALABLE simulation speed
l = Keep the constant simulation speed in case of large number of
cores
n How to scale the simulation speed?
l Our target architecture: M-Core
• Tile architecture with 2D mesh network
Partitioning of the target processor into multiple FPGAs
Partition
Many-core
Processor
9. Proposal of ScalableCore
n Multiple FPGAs corresponding to the target processor
l Each ScalableCore Unit has a part of the target processor
and shares the simulation progress with its neighbor Units
ScalableCore Unit
(FPGA Card with off-chip Memory)
A part of the target processor
ScalableCore Board
Connecting among
the ScalableCore Units
LCD Display
for simulation information
Target Core
System
Function
Target Processor (M-Core)
10. Simulation Target Manycore: M-Core
n Tile architecture with 2D mesh network
l A Node has: Core, Local Memory, INCC (DMA controller) and Router
l Local Memory: Independent Address Space, Data transfer by DMAs
DRAM Controller DRAM Controller
Local
Memory
Core
INCC
R
Node
Current Target of
ScalableCore system
DRAM Controller DRAM Controller
11. ScalableCore system 1.1: Overview
n Simulating the M-Core with up to 64 Nodes (= FPGAs)
Local
Memory
Core
INCC
R
System Functions
Able to increase/decrease
the number of Nodes
15. 64 Nodes (8×8) : 64 ScalableCore Units
Scalable Extension!
16. ScalableCore system 1.1: Components
n ScalableCore Unit
FPGA board with off-chip SRAM
l Xilinx Spartan-3E XC3S500E
l 512KBi SRAM (8bit, 1 port for read/write)
l Configuration ROM
n ScalableCore Board
Interface board bridging Units
l Power regulator & SD card slot
17. ScalableCore system 1.1:Logic Hierarchy
Core INCC Router
Local Memory
Target Core (Interface)
(a Node in M-Core)
Interface Register Arbiter
System Functions
Memory Multiplexer Ser/Des
Device Controller Initializer
18. ScalableCore system 1.1:Logic Architecture
Off-chip
SRAM
SRAM Controller SD Card Controller Devices
Node Memory
Memory Controller DMA Register
SD
Memory Multiplexer
IR IR IR IR
Configuration
ROM JTAG
Memory DMA XCF04S port
Fetch Unit Generator/
Access Unit
Receiver
INCC
Register Interface Interface
Decoder
File Register Register
Router
Execution Unit Arbiter
Core
State Machine Controller IR IR
XBAR
to/from
Adjacent Units
Clock
Ser/Des
Reset
Ser/Des
IR Ser/Des
ScalableCore Unit
FPGA Spartan-3E Ser/Des
19. Two key techniques
n Local Barrier Synchronization
l Each FPGA has one Node of M-Core (or other tile architecture)
l To satisfy the cycle accuracy, hand shaking of simulation state is
needed
• All-to-All hand shake: Increasing overhead to the number of cores
l Our target is a tile architecture, so …
Hand shaking by only 4 neighbors
n Virtual Cycle
l How to emulate the complex hardware?
• Ex.) larger number of memory ports
Use multiple FPGA cycles for 1 target cycle
20. Local Barrier Synchronization
n Handshakes with 4 neighbor FPGAs
l Constant handshaking overhead, not increasing with the
increasing of # target cores
l So it achieves scalable simulation speed
Sending to Unit 0 Sending to Unit 0
Sending to Unit 1 Sending to Unit 1
0 Sending to Unit 2 Sending to Unit 2
Sending to Unit 3 Sending to Unit 3
3 4 1 Receiving from Unit 0 Receiving from Unit 0
Receiving from Unit 1 Receiving from Unit 1
Receiving from Unit 2 Receiving from Unit 2
2 Receiving from Unit 3 Receiving from Unit 3
Cycle 1 Cycle 2
21. Virtual Cycle
n Multiple FPGA clock cycles for 1 target clock cycle
l Virtually complex hardware by using simple FPGA equipment
• Example. Multiport RAM by driving 1 port RAM multiple times
Drive the circuit of target components
Core
Proceeding INCC
Target Circuit State Router Process the memory accesses
Interleaved
Core (IF) Core (L/S) INCC Send INCC Recv
Memory Access
via Memory Multiplexer Start sending
Sending the synchronized data via Serial I/O (North)
Data Sender Sending the synchronized data via Serial I/O (East)
via Serial I/Os
…
Sending the synchronized data via Serial I/O (West)
Sending the synchronized data via Serial I/O (South)
Receiving the synchronized data via Serial I/O (North)
Receiving the synchronized data via Serial I/O (East)
Data Receiver
via Serial I/Os Receiving the synchronized data via Serial I/O (West)
Receiving the synchronized data via Serial I/O (South)
Finish synchronization
1 Virtual Cycle
Time
Virtual Cycle Virtual Cycle
N N+1
23. Evaluation: Simulation Speed [K cycle/sec]
n = Clock frequency of the target processor [KHz]
l Software simulator: degrading speed with the increasing of #
target cores
l ScalableCore system: constant speed rate
n Relative Speed
l Increasing # cores, Increasing the relative speed
• In simulation of 64 Nodes, achieves 14.2x speed up
ScalableCore system Software Simulator
16.0 14.2
1200 14.0
1000 1000 1000 1000
Simulation Speed
Relative Speed
1000 12.0 10.4
[K cycle / sec]
800 10.0
8.0 6.7
600
343 6.0
400 2.9
149 4.0
200 96 70 2.0
0 0.0
16 32 48 64 16 32 48 64
# Nodes # Nodes
24. Evaluation: Power [W]
n = Energy consumption of the system per sec
l Software simulator: constant consumption [W]
l ScalableCore system: increasing the power [W]
n Relative Efficiency
(=Ratio of energy used for simulation of 1 clock cycle on the target1)
l More efficient, increasing # target cores
• In simulation of 64 nodes, achieves
25.0 22.2 22.9 23.5
ScalableCore system Software Simulator
Relative Efficiency
19.2
100 84 84 84 84 20.0
80 15.0
Power [W]
60 51
38 10.0
40 26
13 5.0
20
0 0.0
16 32 48 64 16 32 48 64
# Nodes # Nodes
25. Conclusion
n ScalableCore system 1.1
An FPGA-based scalable simulation system
for tile architecture evaluations
l Multiple FPGAs
l Two key techniques
• Virtual cycle
• Local Barrier Synchronization
l 14.2 times faster simulation than the software simulator
• When simulating the more detailed architecture the speedup rate
becomes the very larger
n Future Work
l Off-chip DRAM support
l Virtual combined multiple FPGAs for a large core
l Time-multiplexed driven for higher hardware utilization