PIMBALL: Binary Neural Networks in Spintronic Memory

Salonik Resch; S. Karen Khatamifard; Zamshed Iqbal Chowdhury; Masoud Zabihi; Zhengyang Zhao; Jian-Ping Wang; Sachin S. Sapatnekar; Ulya R. Karpuzcu

arXiv · 1812.03989

PIMBALL: Binary Neural Networks in Spintronic Memory

Salonik Resch·S. Karen Khatamifard·Zamshed Iqbal Chowdhury·Masoud Zabihi·Zhengyang Zhao·Jian-Ping Wang·Sachin S. Sapatnekar·Ulya R. Karpuzcu

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Abstract

Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency or hardware/software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU, GPU, and FPGA based implementations while delivering higher throughput.

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Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury, Masoud Zabihi, Zhengyang Zhao, Jian-Ping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu. 2018-12-10. PIMBALL: Binary Neural Networks in Spintronic Memory. https://arxiv.org/abs/1812.03989

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