Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge

• URL: http://arxiv.org/abs/2606.19964v1
• Date: Thu, 18 Jun 2026 09:05:41 GMT
• Title: Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
• Abstract summary: Tsetlin Machine (TM) is a logic-based machine learning approach that relies on simple bitwise operations and finite-state automata.<n>Recent work has focused on co-processor and accelerator designs based on TM.<n>We present a domain-specific RISC-V microprocessor architecture and design flow tailored for TM inference.
• Score: 1.2285549481077163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tsetlin Machine (TM) is a logic-based machine learning approach that relies on simple bitwise operations and finite-state automata, which makes it attractive for edge AI deployments. Recent work has focused on co-processor and accelerator designs based on Tsetlin Machines (TMs). Although these designs achieve high performance, they typically depend on tightly coupled interfaces, microcode-style programming, and external host processors, limiting flexibility and ease of programming. In this work, we present a domain-specific RISC-V microprocessor architecture and design flow tailored for TM inference. Leveraging the modular structure of RISC-V, we design a reduced instruction subset processor that retains programmability while targeting improved performance and lower energy consumption for TM workloads. Instruction profiling is employed to guide instruction reduction, followed by datapath and control path simplifications tailored to TM inference. Both the baseline RV32IM core and the proposed reduced core are evaluated across multiple datasets and compared with Binarized Neural Networks (BNNs), which serve as a hardware-efficient baseline due to their reliance on bitwise operations during inference. Results show that TM achieves comparable or higher accuracy (e.g., up to 88.18% on CIFAR-2 compared to 60.0% for BNN) while reducing execution time by up to 98% across multiple datasets. Furthermore, the proposed design achieves an average $29.7\times$ reduction in energy consumption, demonstrating its effectiveness for programmable and efficient edge AI systems.

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