IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin Machines

Autor:	Ghazal, Omar, Singh, Simranjeet, Rahman, Tousif, Yu, Shengqi, Zheng, Yujin, Balsamo, Domenico, Patkar, Sachin, Merchant, Farhad, Xia, Fei, Yakovlev, Alex, Shafik, Rishad
Rok vydání:	2023
Předmět:	Computer Science - Hardware Architecture Computer Science - Artificial Intelligence Computer Science - Emerging Technologies Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and storage capabilities showing promising performance for ML applications. However, ReRAM devices have design challenges, such as non-linear digital-analog conversion and circuit overheads. This paper proposes an In-Memory Boolean-to-Current Inference Architecture (IMBUE) that uses ReRAM-transistor cells to eliminate the need for such conversions. IMBUE processes Boolean feature inputs expressed as digital voltages and generates parallel current paths based on resistive memory states. The proportional column current is then translated back to the Boolean domain for further digital processing. The IMBUE architecture is inspired by the Tsetlin Machine (TM), an emerging ML algorithm based on intrinsically Boolean logic. The IMBUE architecture demonstrates significant performance improvements over binarized convolutional neural networks and digital TM in-memory implementations, achieving up to a 12.99x and 5.28x increase, respectively. Comment: Accepted at ACM/IEEE International Symposium on Low Power Electronics and Design 2023 (ISLPED 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.12914 Zobrazit plný text záznamu View this record from Arxiv