Flex-PE: Flexible and SIMD Multiprecision Processing Element for AI Workloads

Abstract

The rapid evolution of artificial intelligence (AI) models, from deep neural networks (DNNs) to transformers/large-language models (LLMs), demands flexible hardware solutions to meet diverse execution needs across edge and cloud platforms. Existing accelerators lack unified support for multiprecision arithmetic and runtime-configurable activation functions (AFs). This work proposes Flex-PE, a single instruction, multiple data (SIMD)-enabled multiprecision processing element that efficiently integrates multiply-and-accumulate operations with configurable AFs using unified hardware, including Sigmoid, Tanh, ReLU, and SoftMax. The proposed design achieves throughput improvements of up to $16\times $ FxP4, $8\times $ FxP8, $4\times $ FxP16, and $1\times $ FxP32, with maximum hardware efficiency for both iterative and pipelined architectures. An area-efficient iterative Flex-PE-based SIMD systolic array reduces DMA reads by up to $62\times $ and $371\times $ for input feature maps and weight filters in VGG-16, achieving 8.42 GOPS/W energy efficiency with minimal accuracy loss (<2%). Flex-PE scales from 4-bit edge inference to FxP8/16/32, supporting edge and cloud high-performance computing (HPC) while providing high-performance adaptable AI hardware with optimal precision, throughput, and energy efficiency.