From plasma to beefarm: Design experience of an FPGA-based multicore prototype

In this paper, we take a MIPS-based open-source uniprocessor soft core, Plasma, and extend it to obtain the Beefarm infrastructure for FPGA-based multiprocessor emulation, a popular research topic of the last few years both in the FPGA and the computer architecture communities. We discuss various design tradeoffs and we demonstrate superior scalability through experimental results compared to traditional software instruction set simulators. Based on our experience of designing and building a complete FPGA-based multiprocessor emulation system that supports run-time and compiler infrastructure and on the actual executions of our experiments running Software Transactional Memory (STM) benchmarks, we comment on the pros, cons and future trends of using hardware-based emulation for research.Peer ReviewedPostprint (author's final draft

VThreads: A novel VLIW chip multiprocessor with hardware-assisted PThreads

We discuss VThreads, a novel VLIW CMP with hardware-assisted shared-memory Thread support. VThreads supports Instruction Level Parallelism via static multiple-issue and Thread Level Parallelism via hardware-assisted POSIX Threads along with extensive customization. It allows the instantiation of tightlycoupled streaming accelerators and supports up to 7-address Multiple-Input, Multiple-Output instruction extensions. VThreads is designed in technology-independent Register-Transfer-Level VHDL and prototyped on 40 nm and 28 nm Field-Programmable gate arrays. It was evaluated against a PThreads-based multiprocessor based on the Sparc-V8 ISA. On a 65 nm ASIC implementation VThreads achieves up to x7.2 performance increase on synthetic benchmarks, x5 on a parallel Mandelbrot implementation, 66% better on a threaded JPEG implementation, 79% better on an edge-detection benchmark and ~13% improvement on DES compared to the Leon3MP CMP. In the range of 2 to 8 cores VThreads demonstrates a post-route (statistical) power reduction between 65% to 57% at an area increase of 1.2%-10% for 1-8 cores, compared to a similarly-configured Leon3MP CMP. This combination of micro-architectural features, scalability, extensibility, hardware support for low-latency PThreads, power efficiency and area make the processor an attractive proposition for low-power, deeply-embedded applications requiring minimum OS support

Customizable vector acceleration in extreme-edge computing. A risc-v software/hardware architecture study on VGG-16 implementation

Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the Internet of Things (IoT) hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied to particular computation kernels, even within the same application. Vector-oriented hardware acceleration has gained renewed interest to support artificial intelligence (AI) applications like convolutional networks or classification algorithms. We present a comprehensive investigation of the performance and power efficiency achievable by configurable vector acceleration subsystems, obtaining evidence of both the high potential of the proposed microarchitecture and the advantage of hardware customization in total transparency to the software program

ECC Memory for Fault Tolerant RISC-V Processors

Numerous processor cores based on the popular RISC-V Instruction Set Architecture have been developed in the past few years and are freely available. The same applies for RISC-V ecosystems that allow to implement System-on-Chips with RISC-V processors on ASICs or FPGAs. However, so far only very little concepts and implementations for fault tolerant RISC-V processors are existing. This inhibits the use of RISC-V for safety-critical applications (as in the automotive domain) or within radiation environments (as in the aerospace domain). This work enhances the existing implementations Rocket and BOOM with a generic Error Correction Code (ECC) protected memory as a first step towards fault tolerance. The impact of the ECC additions on performance and resource utilization are discussed

BRISC-V: An Open-Source Architecture Design Space Exploration Toolbox

In this work, we introduce a platform for register-transfer level (RTL) architecture design space exploration. The platform is an open-source, parameterized, synthesizable set of RTL modules for designing RISC-V based single and multi-core architecture systems. The platform is designed with a high degree of modularity. It provides highly-parameterized, composable RTL modules for fast and accurate exploration of different RISC-V based core complexities, multi-level caching and memory organizations, system topologies, router architectures, and routing schemes. The platform can be used for both RTL simulation and FPGA based emulation. The hardware modules are implemented in synthesizable Verilog using no vendor-specific blocks. The platform includes a RISC-V compiler toolchain to assist in developing software for the cores, a web-based system configuration graphical user interface (GUI) and a web-based RISC-V assembly simulator. The platform supports a myriad of RISC-V architectures, ranging from a simple single cycle processor to a multi-core SoC with a complex memory hierarchy and a network-on-chip. The modules are designed to support incremental additions and modifications. The interfaces between components are particularly designed to allow parts of the processor such as whole cache modules, cores or individual pipeline stages, to be modified or replaced without impacting the rest of the system. The platform allows researchers to quickly instantiate complete working RISC-V multi-core systems with synthesizable RTL and make targeted modifications to fit their needs. The complete platform (including Verilog source code) can be downloaded at https://ascslab.org/research/briscv/explorer/explorer.html.Comment: In Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA '19

High-speed dynamic partial reconfiguration for field programmable gate arrays

With dynamically and partially reconfigurable designs, it is necessary that the speed of the reconfiguration be accomplished in a time that is sufficiently small such that the operation of reconfiguration is not the limiting factor in the process. Therefore, the communication between the source of configuration and the configurable unit must be made as fast as possible. The aim of this work is to use an embedded controller internal to the FPGA to control the reconfiguration process and obtain the maximum speed at which reconfiguration can occur, with current FPGA technology. The use of Direct Memory Access (DMA) driven operations instead of the current arbitrated bus architectures yielded a 30% increase in the speed of reconfiguration compared to other methods such as OPB_HWICAP and PLB_HWICAP [1]. The use of interrupt driven partial reconfiguration was also introduced, allowing the processor to switch to other tasks during the reconfiguration operation. All of these contributions lead to significant performance improvements over current partial reconfiguration subsystems. The configuration controller was tested using four partially reconfigurable system implementations: (i) one targeting the Hard IP PowerPC405 on Virtex-4, (ii) a second targeting the Soft IP MicroBlaze on Virtex-5, (iii) a third targeting the Hard IP PowerPC440 on Virtex-5, and (iv) a fourth system targets the Hard IP PowerPC440 on Virtex-5 capable of adaptive feedback. The adaptive feedback Virtex-5 system can use internal voltage and temperature measurements from the Xilinx System Monitor IP to dynamically increase or decrease the speed of reconfiguration and/or change other reconfigurable aspects of the system to better match the environment

FPGA-Based Processor Acceleration for Image Processing Applications

FPGA-based embedded image processing systems offer considerable computing resources but present programming challenges when compared to software systems. The paper describes an approach based on an FPGA-based soft processor called Image Processing Processor (IPPro) which can operate up to 337 MHz on a high-end Xilinx FPGA family and gives details of the dataflow-based programming environment. The approach is demonstrated for a k-means clustering operation and a traffic sign recognition application, both of which have been prototyped on an Avnet Zedboard that has Xilinx Zynq-7000 system-on-chip (SoC). A number of parallel dataflow mapping options were explored giving a speed-up of 8 times for the k-means clustering using 16 IPPro cores, and a speed-up of 9.6 times for the morphology filter operation of the traffic sign recognition using 16 IPPro cores compared to their equivalent ARM-based software implementations. We show that for k-means clustering, the 16 IPPro cores implementation is 57, 28 and 1.7 times more power efficient (fps/W) than ARM Cortex-A7 CPU, nVIDIA GeForce GTX980 GPU and ARM Mali-T628 embedded GPU respectively