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.
SHA-256 is a fundamental hashing algorithm in embedded security, authentication, data integrity verification, and hardware random number generation in resource-constrained systems. This paper presents 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. Throughput, execution time, current draw, and energy per kilobyte were measured across software and hardware-accelerated modes with and without Direct Memory Access on both cores, and against a software baseline on the predecessor RP2040. Hardware acceleration yielded speedups of 14.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} and 13.58\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} over software for the ARM and RISC-V cores respectively, with energy per KB reduced from approximately 25 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\upmu }$$\end{document}J/KB to under 1.79 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\upmu }$$\end{document}J/KB in both cases, while current draw remained stable between 17 and 18 mA across all configurations, indicating that efficiency gains are attributable to reduced execution time rather than lower active current. A counter-intuitive observation emerges from the DMA analysis: despite the ARM ecosystem’s significantly greater maturity in embedded bus integration, the ARM Cortex-M33 exhibits a larger DMA-induced throughput penalty (18.1%, factor of 1.221\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}) than the newer RISC-V Hazard3 core (13.6%, factor of 1.157\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}) when accessing the shared SHA-256 peripheral on identical silicon. To our knowledge, this is the first same-silicon comparison of ARM and RISC-V cryptographic acceleration with direct energy measurements, and the first published per-configuration energy characterization of the RP2350.
Atul Ravi, S. S., B. Kailash et al.· Discover Electronics· 0 citations
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 PicoRV32 scalar core and two memory-mapped hardware accelerators: FPSQRT core and a reconfigurable CRC-32 integrity engine. Using a customized three-state AXI4-Lite FSM-based interface, our design effectively separates the acceleration compute latency from that of the bus timings enabling bare-metal firmware to perform its coprocessor computations without OS intervention. Simulation results show performance speed-ups of 1.96x and 9x for the FPSQRT and CRC-32 respectively giving a total speedup of 12x relative to software only. Post-implementation synthesis on the Xilinx Zynq-7000 family FPGA (XC7Z020) shows full timing closure at a clock frequency of 100 MHz with WNS of +7.058 ns. Resource utilization figures are exceptionally low with the design requiring only 5,208 Slice LUT (9.8%), and 11.4% of block RAM with total on-chip power dissipation of 1.694 W. Our hardware-in-the-loop test on the Digilent ZedBoard confirmed the functional accuracy and operational stability with bare-metal operation status confirmed by LED (T21, T22) and a serial console.
P.anuradha, Vulapala Tejashwi, Meenugu Venkateshwarlu et al.· 2026 7th International Confe...· 0 citations
Embedded systems increasingly require on-device deep learning, yet their processors must simultaneously handle real-time sensing, networking administration, and data control. Existing Field-Programmable Gate Array (FPGA) accelerators typically target peak per-operator throughput without addressing concurrent execution demands of real-time embedded platforms. This paper presents 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. Unlike traditional accelerators optimized for peak per-operator speed, this design prioritizes asynchronous co-executionbetween the processing system (PS, the dual-core Cortex-A9) and the programmable logic (PL): The PL performs tiled matrix multiplication, while the PS executes preprocessing, orchestration, and I/O data concurrently, increasing effective end-to-end throughput regardless of the relative isolated performance of CPU and FPGA. The proposed module includes high-level-synthesis (HLS)-based matrix multiplication, activation functions, and Advanced eXtensible Interface (AXI)-Stream Direct Memory Access (DMA) interfaces, wrapped as custom PyTorch kernels under the PetaLinux operating system. The results obtained on the PYNQ-Z2 board show that, once the DMA transfer time is included in the measurement, the FPGA path does not surpass Cortex-A9 in isolated per-operator latencies across the evaluated range; the benefit lies instead in delegating the matrix compute to the fabric at low incremental power while the host CPU cores stay available for concurrent tasks. A concurrent workload sweep across matrix sizes from 8×8 to 256×256 confirms that the co-execution mode sustains 98–99% of available PS compute throughput compared with a constant ≈50% in single-core blocking mode; the difference is statistically significant for all evaluated sizes (see Mann–Whitney U: U=25, p=3.97×10−3, perfect discrimination, n=5). A fair dual-core CPU-only baseline attains comparable PS availability, so this figure reflects the dual-core scheduling that co-execution enables rather than a per-operator advantage of the fabric; the accelerator’s distinct role is to perform the matrix arithmetic off the general-purpose cores at low incremental power. The design occupies only 8% of available look-up tables (LUTs) and 5% of digital signal processing (DSP) blocks, maintains 1.69 W power with a junction temperature of 44.5 °C, and achieves 96.10% MNIST accuracy under fixed-point arithmetic.
Omar Hernandez-Yañez, A. Juárez-Lora, J. Y. Montiel-Pérez et al.· Electronics· 0 citations
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 sit alongside a RISC-V core inside a resource-limited SoC. At its core is an 8 × 8 weightstationary systolic array capable of 64 multiply-accumulate (MAC) operations per clock cycle, fed by a four-state DMA controller that moves matrix tiles into two on-chip SRAM buffers without CPU involvement, so the array rarely stalls waiting on data. The RTL was verified in Verilator with cycle-accurate simulation, then carried through Cadence Genus synthesis on a SCL 180 nm standard cell library. The result is a 500 MHz design occupying about $58,236 \mu \mathrm{m}^{2}$ of silicon and reaching a measured peak of $32 \text{GMAC} / \mathrm{s}$ - figures that we believe justify this class of accelerator for tightly area-constrained edge deployments.
Sriskanda Cp, Sucheth N, Rajeev Pankaj Nelapati· 2026 International Conferenc...· 0 citations
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 SQRT CSLAs rely on redundant Ripple Carry Adders (RCAs) to compute parallel potential sums for both $\mathbf{C}_{\mathbf{i n}}=\mathbf{0}$ and $\mathbf{C}_{\mathbf{i n}}=\mathbf{1}$ conditions, leading to excessive dynamic switching activity. This paper proposes a highly power-efficient 16-bit MAC architecture utilizing a Carry Enable Binary to Excess-1 Converter (CEBEC) SQRT CSLA. The proposed design entirely eliminates the redundant $\mathbf{C}_{\text {in }} \boldsymbol{=} \mathbf{1}$ RCA blocks, replacing them with a streamlined combinational logic path. This path utilizes optimized NOT and XOR gates for lower-order bits, coupled with targeted OR-gate logic at the Most Significant Bit (MSB) for rapid carry evaluation. The baseline and proposed architectures were functionally verified via Cadence SimVision and synthesized to the gate level using the Cadence Genus Synthesis Solution. Post-synthesis power and area analysis demonstrates that the proposed CEBEC-based MAC unit achieves a significant 36.7% reduction in dynamic switching power compared to the baseline CSLA, dropping from 15.22 $\boldsymbol{\mu} \mathbf{W}$ to $\mathbf{9. 6 3} \boldsymbol{\mu} \mathbf{W}$. While this hardware optimization trades a marginal 5.3% increase in total standard cell area, the substantial mitigation of switching activity makes the proposed architecture highly viable for low-power DSP ASICs.
Shriparna Praveenkumar Hegde, V. D, Manjunath G. Asuti et al.· 2026 5th International Confe...· 0 citations
This study introduces a new, energy-efficient, fully analog integrated architecture of the Learning Vector Quantization algorithm. The design showcases its adeptness in effectively managing multiple input features, ensuring high precision, and minimizing power consumption. The main components of the algorithm are Gaussian function and argmax operator circuits. The operational concepts of the architecture are elaborated in detail and are applied to a power-efficient (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.28 \mu W$$\end{document}) configuration with a low voltage (0.6V) setup. This implementation is tailored for two distinct classification tasks: digit recognition and bearing fault condition monitoring. utilizing a 90nm CMOS process, employing the Cadence IC Suite for both the schematic and physical design stages. Comparative analysis of post-layout simulation results with an equivalent software based classifier affirms the accuracy of the modeling and design methodologies employed.
Vassilis Alimisis, E. Serlis, Georgios Gennis et al.· Analog Integrated Circuits a...· 0 citations
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