SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency

• URL: http://arxiv.org/abs/2601.17279v1
• Date: Sat, 24 Jan 2026 03:38:11 GMT
• Title: SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency
• Authors: Sonu Kumar, Lavanya Vinnakota, Mukul Lokhande, Santosh Kumar Vishvakarma, Adam Teman,
• Abstract summary: This work presents SPADE, a unified multi-precision SIMD Posit-based multiplyaccumulate (MAC) architecture.<n>Unlike prior single-precision or floating/fixed-point SIMD MACs, SPADE introduces a regime-aware, lane-fused SIMD Posit datapath.<n> FPGA implementation on a Xilinx Virtex-7 shows 45.13% LUT and 80% slice reduction for Posit (8,0), and up to 28.44% and 17.47% improvement for Posit (16,1) and Posit (32,2) over prior work.
• Score: 0.12314765641075437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing demand for edge-AI systems requires arithmetic units that balance numerical precision, energy efficiency, and compact hardware while supporting diverse formats. Posit arithmetic offers advantages over floating- and fixed-point representations through its tapered precision, wide dynamic range, and improved numerical robustness. This work presents SPADE, a unified multi-precision SIMD Posit-based multiplyaccumulate (MAC) architecture supporting Posit (8,0), Posit (16,1), and Posit (32,2) within a single framework. Unlike prior single-precision or floating/fixed-point SIMD MACs, SPADE introduces a regime-aware, lane-fused SIMD Posit datapath that hierarchically reuses Posit-specific submodules (LOD, complementor, shifter, and multiplier) across 8/16/32-bit precisions without datapath replication. FPGA implementation on a Xilinx Virtex-7 shows 45.13% LUT and 80% slice reduction for Posit (8,0), and up to 28.44% and 17.47% improvement for Posit (16,1) and Posit (32,2) over prior work, with only 6.9% LUT and 14.9% register overhead for multi-precision support. ASIC results across TSMC nodes achieve 1.38 GHz at 6.1 mW (28 nm). Evaluation on MNIST, CIFAR-10/100, and alphabet datasets confirms competitive inference accuracy.

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