High throughput accelerator interface framework for a linear time-multiplexed FPGA overlay

Coarse-grained FPGA overlays improve design productivity through software-like programmability and fast compilation. However, the effectiveness of overlays as accelerators is dependent on suitable interface and programming integration into a typically processor-based computing system, an aspect which has often been neglected in evaluations of overlays. We explore the integration of a time-multiplexed FPGA overlay over a server-class PCI Express interface. We show how this integration can be optimised to maximise performance, and evaluate the area overhead. We also propose a user-friendly programming model for such an overlay accelerator system

A RISC-V-based FPGA Overlay to Simplify Embedded Accelerator Deployment

Modern cyber-physical systems (CPS) are increasingly adopting heterogeneous systems-on-chip (HeSoCs) as a computing platform to satisfy the demands of their sophisticated workloads. FPGA-based HeSoCs can reach high performance and energy efficiency at the cost of increased design complexity. High-Level Synthesis (HLS) can ease IP design, but automated tools still lack the maturity to efficiently and easily tackle system-level integration of the many hardware and software blocks included in a modern CPS. We present an innovative hardware overlay offering plug-and-play integration of HLS-compiled or handcrafted acceleration IPs thanks to a customizable wrapper attached to the overlay interconnect and providing shared-memory communication to the overlay cores. The latter are based on the open RISC-V ISA and offer simplified software management of the acceleration IP. Deploying the proposed overlay on a Xilinx ZU9EG shows ≈ 20% LUT usage and ≈ 4× speedup compared to program execution on the ARM host core

Balancing Design Options with Sherpa

Application specific processors offer the potential of rapidly designed logic specifically constructed to meet the performance and area demands of the task at hand. Recently, there have been several major projects that attempt to automate the process of transforming a predetermined processor configuration into a low level description for fabrication. These projects either leave the specification of the processor to the designer, which can be a significant engineering burden, or handle it in a fully automated fashion, which completely removes the designer from the loop. In this paper we introduce a technique for guiding the design and optimization of application specific processors. The goal of the Sherpa design framework is to automate certain design tasks and provide early feedback to help the designer navigate their way through the architecture design space. Our approach is to decompose the overall problem of choosing an optimal architecture into a set of sub-problems that are, to the first order, independent. For each subproblem, we create a model that relates performance to area. From this, we build a constraint system that can be solved using integer-linear programming techniques, and arrive at an ideal parameter selection for all architectural components. Our approach only takes a few minutes to explore the design space allowing the designer or compiler to see the potential benefits of optimizations rapidly. We show that the expected performance using our model correlates strongly to detailed pipeline simulations, and present results showing design tradeoffs for several different benchmarks

Lagarto I RISC-V Multi-core: Research Challenges to Build and Integrate a Network-on-Chip

Current compute-intensive applications largely exceed the resources of single-core processors. To face this problem, multi-core processors along with parallel computing techniques have become a solution to increase the computational performance. Likewise, multi-processors are fundamental to support new technologies and new science applications challenges. A specific objective of the Lagarto project developed at the National Polytechnic Institute of Mexico is to generate an ecosystem of high-performance processors for the industry and HPC in Mexico, supporting new technologies and scientific applications. This work presents the first approach of the Lagarto project to the design of multi-core processors and the research challenges to build an infrastructure that allows the flagship core of the Lagarto project to scale to multi- and many-cores. Using the OpenPiton platform with the Ariane RISC-V core, a functional tile has been built, integrating a Lagarto I core with memory coherence that executes atomic instructions, and a NoC that allows scaling the project to many-core versions. This work represents the initial state of the design of mexican multi-and many-cores processors

The AXIOM software layers

AXIOM project aims at developing a heterogeneous computing board (SMP-FPGA).The Software Layers developed at the AXIOM project are explained.OmpSs provides an easy way to execute heterogeneous codes in multiple cores. People and objects will soon share the same digital network for information exchange in a world named as the age of the cyber-physical systems. The general expectation is that people and systems will interact in real-time. This poses pressure onto systems design to support increasing demands on computational power, while keeping a low power envelop. Additionally, modular scaling and easy programmability are also important to ensure these systems to become widespread. The whole set of expectations impose scientific and technological challenges that need to be properly addressed.The AXIOM project (Agile, eXtensible, fast I/O Module) will research new hardware/software architectures for cyber-physical systems to meet such expectations. The technical approach aims at solving fundamental problems to enable easy programmability of heterogeneous multi-core multi-board systems. AXIOM proposes the use of the task-based OmpSs programming model, leveraging low-level communication interfaces provided by the hardware. Modular scalability will be possible thanks to a fast interconnect embedded into each module. To this aim, an innovative ARM and FPGA-based board will be designed, with enhanced capabilities for interfacing with the physical world. Its effectiveness will be demonstrated with key scenarios such as Smart Video-Surveillance and Smart Living/Home (domotics).Peer ReviewedPostprint (author's final draft

A RECONFIGURABLE AND EXTENSIBLE EXPLORATION PLATFORM FOR FUTURE HETEROGENEOUS SYSTEMS

Accelerator-based -or heterogeneous- computing has become increasingly important in a variety of scenarios, ranging from High-Performance Computing (HPC) to embedded systems. While most solutions use sometimes custom-made components, most of today’s systems rely on commodity highend CPUs and/or GPU devices, which deliver adequate performance while ensuring programmability, productivity, and application portability. Unfortunately, pure general-purpose hardware is affected by inherently limited power-efficiency, that is, low GFLOPS-per-Watt, now considered as a primary metric. The many-core model and architectural customization can play here a key role, as they enable unprecedented levels of power-efficiency compared to CPUs/GPUs. However, such paradigms are still immature and deeper exploration is indispensable. This dissertation investigates customizability and proposes novel solutions for heterogeneous architectures, focusing on mechanisms related to coherence and network-on-chip (NoC). First, the work presents a non-coherent scratchpad memory with a configurable bank remapping system to reduce bank conflicts. The experimental results show the benefits of both using a customizable hardware bank remapping function and non-coherent memories for some types of algorithms. Next, we demonstrate how a distributed synchronization master better suits many-cores than standard centralized solutions. This solution, inspired by the directory-based coherence mechanism, supports concurrent synchronizations without relying on memory transactions. The results collected for different NoC sizes provided indications about the area overheads incurred by our solution and demonstrated the benefits of using a dedicated hardware synchronization support. Finally, this dissertation proposes an advanced coherence subsystem, based on the sparse directory approach, with a selective coherence maintenance system which allows coherence to be deactivated for blocks that do not require it. Experimental results show that the use of a hybrid coherent and non-coherent architectural mechanism along with an extended coherence protocol can enhance performance. The above results were all collected by means of a modular and customizable heterogeneous many-core system developed to support the exploration of power-efficient high-performance computing architectures. The system is based on a NoC and a customizable GPU-like accelerator core, as well as a reconfigurable coherence subsystem, ensuring application-specific configuration capabilities. All the explored solutions were evaluated on this real heterogeneous system, which comes along with the above methodological results as part of the contribution in this dissertation. In fact, as a key benefit, the experimental platform enables users to integrate novel hardware/software solutions on a full-system scale, whereas existing platforms do not always support a comprehensive heterogeneous architecture exploration

A Survey on IEEE 1588 Implementation for RISC-V Low-Power Embedded Devices

IEEE 1588, also known as the Precision Time Protocol (PTP), is a standard protocol for clock synchronization in distributed systems. While it is not architecture-specific, implementing IEEE 1588 on Reduced Instruction Set Computer-V (RISC-V) low-power embedded devices demands considering the system requirements and available resources. This paper explores various approaches and techniques to achieve accurate time synchronization in such instruments. The analysis covers software and hardware implementations, discussing each method’s challenges, benefits, and trade-offs. By examining the state-of-the-art in this field, this paper provides valuable insights and guidance for researchers and engineers working on time-critical applications in RISC-V-based embedded systems, aiding in selecting the most-suitable stack for their designs.This work was partially supported by the ECSEL Joint Undertaking in the H2020 project IMOCO4.E, grant agreement No.10100731, and by the Basque Government within the fund for research groups of the Basque University System IT1440-22 and KK-2023/00015

Review of open neuromorphic architectures and a first integration in the RISC-V PULP platform

Although initially conceived as a tool to empower neuroscientific research by emulating and simulating the human brain, Spiking Neural Networks (SNNs), also known as third generation neural networks, are gaining popularity for their low-power and sparse data processing capabilities. These attributes are valuable for power-constrained edge and Internet of Things (IoT) applications. Several open-source FPGA and ASIC neuromorphic processors have been developed to explore thisfield, although they often require additional computing elements to manage data and communications. In this work, we review recent open-source neuromorphic architectures and the PULP ecosystem. We then present an integration of the ReckOn digitalneuromorphic processor with the PULPissimo RISC-V single core microcontroller to enable edge IoT applications. Our integrated design is validated through QuestaSim hardware simulation. Through this integration of low-power neuromorphic and RISC-V processors, we focus on the promising potential of SNNs for optimizing edge IoT systems constrained by power budgets and data sparsity

A comparative survey of open-source application-class RISC-V processor implementations

The numerous emerging implementations of RISC-V processors and frameworks underline the success of this Instruction Set Architecture (ISA) specification. The free and open source character of many implementations facilitates their adoption in academic and commercial projects. As yet it is not easy to say which implementation fits best for a system with given requirements such as processing performance or power consumption. With varying backgrounds and histories, the developed RISC-V processors are very different from each other. Comparisons are difficult, because results are reported for arbitrary technologies and configuration settings. Scaling factors are used to draw comparisons, but this gives only rough estimates. In order to give more substantiated results, this paper compares the most prominent open-source application-class RISC-V projects by running identical benchmarks on identical platforms with defined configuration settings. The Rocket, BOOM, CVA6, and SHAKTI C-Class implementations are evaluated for processing performance, area and resource utilization, power consumption as well as efficiency. Results are presented for the Xilinx Virtex UltraScale+ family and GlobalFoundries 22FDX ASIC technology