NL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference

Lei Zhao; Luca Buonanno; Archit Gajjar; John Moon; Aishwarya Natarajan; Sergey Serebryakov; Ron M. Roth; Xia Sheng; Youtao Zhang; Paolo Faraboschi; Jim Ignowski; Giacomo Pedretti

arXiv · 2511.13950

NL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference

Lei Zhao·Luca Buonanno·Archit Gajjar·John Moon·Aishwarya Natarajan·Sergey Serebryakov·Ron M. Roth·Xia Sheng·Youtao Zhang·Paolo Faraboschi·Jim Ignowski·Giacomo Pedretti

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Abstract

Resistive Random Access Memory (RRAM) based in-memory computing (IMC) accelerators offer significant performance and energy advantages for deep neural networks (DNNs), but face three major limitations: (1) they support only \textit{static} dot-product operations and cannot accelerate arbitrary non-linear functions or data-dependent multiplications essential to modern LLMs; (2) they demand large, power-hungry analog-to-digital converter (ADC) circuits; and (3) mapping model weights to device conductance introduces errors from cell nonidealities. These challenges hinder scalable and accurate IMC acceleration as models grow. We propose NL-DPE, a Non-Linear Dot Product Engine that overcomes these barriers. NL-DPE augments crosspoint arrays with RRAM-based Analog Content Addressable Memory (ACAM) to execute arbitrary non-linear functions and data-dependent matrix multiplications in the analog domain by transforming them into decision trees, fully eliminating ADCs. To address device noise, NL-DPE uses software-based Noise Aware Fine-tuning (NAF), requiring no in-device calibration. Experiments show that NL-DPE delivers 28X energy efficiency and 249X speedup over a GPU baseline, and 22X energy efficiency and 245X speedup over existing IMC accelerators, while maintaining high accuracy.

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Lei Zhao, Luca Buonanno, Archit Gajjar, John Moon, Aishwarya Natarajan, Sergey Serebryakov, Ron M. Roth, Xia Sheng, Youtao Zhang, Paolo Faraboschi, Jim Ignowski, Giacomo Pedretti. 2025-11-17. NL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference. https://arxiv.org/abs/2511.13950

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