Skip to content
#edge computing Open access

FPGA validated RISC V system on chip with a custom systolic array accelerator for edge AI inference

Aug 2026 · Discover Artificial Intelligence · Vol 6 · 0 citations · 29 references
Computer Science

TL;DR

A complete FPGA-validated RISC-V SoC in which a five-stage RV32IM processor works alongside a custom systolic MAC array, and two propositions are formally proved that the output-stationary schedule reduces computation cycles by a naïve, unblocked sequential CPU execution.

Abstract

Running deep-learning inference on constrained edge hardware calls for architectures that cut both latency and memory traffic at once—not one at the expense of the other. This paper describes a complete FPGA-validated RISC-V SoC in which a five-stage RV32IM processor works alongside a custom \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4{\times }4$$\end{document} output-stationary systolic MAC array. An AXI4 crossbar ties the two parts together, and a scatter–gather DMA engine moves operand tiles and results so the processor stays free for control work during inference. Rather than adding custom opcodes, the accelerator is exposed through memory-mapped registers, which means existing RISC-V toolchains need no modification at all. The target device is the Xilinx Artix-7 XC7A35T on a Digilent Basys 3 board. Both Vivado XSim simulation and on-board runs confirm that a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4{\times }4$$\end{document} matrix multiply finishes in 320 ns at 100 MHz, giving a compute-phase throughput of 1.16 GOPS. The systolic compute phase completes in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3N{-}1 = 11$$\end{document} cycles (110 ns); the full end-to-end tile latency including AXI handshaking is 320 ns. After place-and-route the design uses 39.5% LUTs (8214 of 20,800), 17.8% DSP48E1 slices (16 of 90), and 16.0% BRAM (8 of 50) on the XC7A35T, with a total on-chip power of 148 mW and a compute-phase efficiency of 7.86 GOPS/W. The processing elements natively operate on INT8 quantised inputs sign-extended to 18 bits to match the DSP48E1’s narrower multiplier port, with 64-bit accumulators preventing overflow across a full tile. Two propositions are formally proved: the first establishes that the output-stationary schedule reduces computation cycles by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Theta (N^{2}/3)$$\end{document} relative to a naïve, unblocked sequential CPU execution, and the second shows a factor-N reduction in weight-memory reads under the same naïve baseline assumption. The design is validated for functional correctness on physical silicon; power, DMA-overlap, and sustained-throughput figures are estimates derived from Vivado post-implementation analysis and the analytical performance model rather than direct on-board measurement. Among open RISC-V accelerator designs the proposed SoC stands out by combining plain Verilog source, operation on the lower-cost Artix-7 without any embedded ARM host, memory-mapped control that keeps the compiler unmodified, DMA-driven transfers, and a closed-form performance model that matches the measured numbers.

Read PDF

Similar papers

Open access Jul 2026

Comparative energy awareness and performance analysis of ARM and RISC-V SHA-256 hardware acceleration on the RP2350

A quantitative benchmarking study of SHA-256 on the Raspberry Pi RP2350, a microcontroller which has integrated both ARM Cortex-M33 and RISC-V Hazard3 cores on a single die, enabling a direct same-silicon architectural comparison under identical conditions.

Atul Ravi, S. S., B. Kailash et al. · 0 citations
Conference Jul 2026

Bare-Metal Reconfigurable Accelerators for PicoRV32 RISC-V SoC via AXI-Lite Interface on Zynq-7000

The rise in edge computing, and domain-specific workloads, necessitates the development of hardware acceleration with increased flexibility and lower latency that will be tightly coupled to open-source processor cores. In this paper, we explore the design and bare-metal implementation of a RISC-V SoC comprising a PicoR...

P.anuradha, Vulapala Tejashwi, Meenugu Venkateshwarlu et al. · 0 citations
Open access Jul 2026

Asynchronous Co-Execution of PyTorch on Zynq-7000: FPGA Matrix Delegation and PS–PL Overlap for End-to-End Inference Throughput

A systolic array-based accelerator prototype implemented on the Zynq-7000 SoC integrated directly into PyTorch, enabling dense linear algebra to be delegated to the FPGA chip while Cortex-A9 continues executing the software stack uninterrupted.

Omar Hernandez-Yañez, A. Juárez-Lora, J. Y. Montiel-Pérez et al. · 0 citations
Conference Jul 2026

A DMA-Driven Tile-Grid Neural Processing Unit for RISC-V Edge Platforms

Most general purpose processors were never built for the kind of dense arithmetic that neural network inference demands, and running such workloads on a CPU tends to burn power on memory traffic and instruction overhead rather than useful computation. This paper describes a compact neural processing unit (NPU) built to...

Sriskanda Cp, Sucheth N, Rajeev Pankaj Nelapati · 0 citations
Conference Jul 2026

Design and Implementation of a Low-Power 16-Bit MAC Unit using CEBEC SQRT CSLA

Multiply-Accumulate (MAC) units are fundamental hardware blocks in Digital Signal Processing (DSP) systems, where dynamic power efficiency is a critical design constraint. Traditional high-speed MAC architectures frequently employ Square Root Carry Select Adders (SQRT CSLA) for the accumulation stage. However, regular...

Shriparna Praveenkumar Hegde, V. D, Manjunath G. Asuti et al. · 0 citations
Open access Aug 2026

Low-power analog integrated implementation of the learning vector quantization algorithm for interdisciplinary applications

A new, energy-efficient, fully analog integrated architecture of the Learning Vector Quantization algorithm, utilizing a 90nm CMOS process, employing the Cadence IC Suite for both the schematic and physical design stages.

Vassilis Alimisis, E. Serlis, Georgios Gennis et al. · 0 citations

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.