AR-PIM: An Adaptive-Range Processing-in-Memory Architecture

Teyuh Chou, Fernando García-Redondo, Paul N. Whatmough, Zhengya Zhang

Abstract: The crossbar-based processing-in-memory (PIM) architecture has garnered considerable attention for its potential in achieving high energy efficiency for deep neural networks (DNNs). The PIM hardware's accuracy depends heavily on the design and resolution of the analog-to-digital converters (ADCs). Regrettably, high-resolution ADCs tend to be costly and often dominate the overall energy and area of the PIM designs. We propose adaptive-range PIM (AR-PIM) architecture that enables the use of lower-resolution ADCs without sacrificing accuracy. This is achieved by leveraging sparsity in the weights and input activations and dynamically adjusting the number of input activations and distributing MAC operations across multiple cycles during runtime. We perform our evaluations using a commercial 7nm FinFET PDK and show that AR-PIM offers an appealing trade-off, delivering 1.7 × higher energy efficiency and 4.3 × better area benefits without losing accuracy. The latency overhead is modest, only 10% over a baseline PIM architecture.