Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs

• URL: http://arxiv.org/abs/2504.19659v1
• Date: Mon, 28 Apr 2025 10:19:39 GMT
• Title: Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs
• Authors: Muhammad Sabih, Abrarul Karim, Jakob Wittmann, Frank Hannig, Jürgen Teich,
• Abstract summary: We propose novel RISC-V extensions for accelerating DNN models containing semi-structured and unstructured sparsity.<n>Our designs consume a small amount of additional FPGA resources such that the resulting co-designs enable the acceleration of DNNs even on small FPGAs.<n>We benchmark our designs on standard TinyML applications such as keyword spotting, image classification, and person detection.
• Score: 1.4225653519332482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The customizability of RISC-V makes it an attractive choice for accelerating deep neural networks (DNNs). It can be achieved through instruction set extensions and corresponding custom functional units. Yet, efficiently exploiting these opportunities requires a hardware/software co-design approach in which the DNN model, software, and hardware are designed together. In this paper, we propose novel RISC-V extensions for accelerating DNN models containing semi-structured and unstructured sparsity. While the idea of accelerating structured and unstructured pruning is not new, our novel design offers various advantages over other designs. To exploit semi-structured sparsity, we take advantage of the fine-grained (bit-level) configurability of FPGAs and suggest reserving a few bits in a block of DNN weights to encode the information about sparsity in the succeeding blocks. The proposed custom functional unit utilizes this information to skip computations. To exploit unstructured sparsity, we propose a variable cycle sequential multiply-and-accumulate unit that performs only as many multiplications as the non-zero weights. Our implementation of unstructured and semi-structured pruning accelerators can provide speedups of up to a factor of 3 and 4, respectively. We then propose a combined design that can accelerate both types of sparsities, providing speedups of up to a factor of 5. Our designs consume a small amount of additional FPGA resources such that the resulting co-designs enable the acceleration of DNNs even on small FPGAs. We benchmark our designs on standard TinyML applications such as keyword spotting, image classification, and person detection.

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