DSPE: An Energy-Efficient Edge Processor for DeepSeek Inference with MerkleTree-based Incremental Pruning, Multi-Stage Boothing Lookup and Dynamic Adaptive Posit Processing

Yuhan Zhang; Zhou Wang; Zhou Shu; Jiuren Zhou; Yanqing Xu; Xiaonan Tang; Shushan Qiao; Tianchun Ye; Yang Liu; Anil A. Bharath; Emm Mic Drakakis

arXiv · 2605.08615

DSPE: An Energy-Efficient Edge Processor for DeepSeek Inference with MerkleTree-based Incremental Pruning, Multi-Stage Boothing Lookup and Dynamic Adaptive Posit Processing

Yuhan Zhang·Zhou Wang·Zhou Shu·Jiuren Zhou·Yanqing Xu·Xiaonan Tang·Shushan Qiao·Tianchun Ye·Yang Liu·Anil A. Bharath·Emm Mic Drakakis

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Abstract

In recent years, DeepSeek has achieved strong inference performance but remains hard to deploy on energy-constrained edge devices. This paper presents the DeepSeek Processing Element (DSPE), an edge-oriented architecture that alleviates the model's heavy computational and energy demands. DSPE introduces three techniques: the MerkleTree-based Incremental Pruning Scheme (MIPS) for secure redundant-vector reduction, the Multi-Stage Boothing Lookup Method (MBLM) for bit-flip-aware approximate multiplication, and the Dynamic Adaptive Posit Processing Mechanism (DAPPM), which introduces a new DA-Posit format and its corresponding hardware multiplication architecture. Implemented in TSMC 28nm CMOS, DSPE achieves 109.4 TFLOPS/W energy efficiency compared with state-of-the-art designs and offers a scalable foundation for edge deployment.

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Yuhan Zhang, Zhou Wang, Zhou Shu, Jiuren Zhou, Yanqing Xu, Xiaonan Tang, Shushan Qiao, Tianchun Ye, Yang Liu, Anil A. Bharath, Emm Mic Drakakis. 2026-05-09. DSPE: An Energy-Efficient Edge Processor for DeepSeek Inference with MerkleTree-based Incremental Pruning, Multi-Stage Boothing Lookup and Dynamic Adaptive Posit Processing. https://arxiv.org/abs/2605.08615

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