In-Memory Computing for Deep Learning and Beyond

The rise of AI and in particular, deep learning (DL), is a key driver for innovations in computing systems. There is a significant effort towards the design of custom accelerator chips based on reduced precision arithmetic and highly optimized data flow. However, the need to shuttle millions of synaptic weight values between the memory and processing units remains unaddressed. In-memory computing (IMC) is an emerging computing paradigm that addresses this challenge of processor-memory dichotomy. Attributes such as synaptic efficacy and plasticity can be implemented in place by exploiting the physical attributes of memory devices such as phase-change memory (PCM). In this talk, I will give a status update on where in-memory computing stands with respect to DL acceleration. I will present some recent algorithmic advances for performing accurate DL inference and training with imprecise IMC. I will also present state-of-the-art IMC compute cores based on PCM fabricated in 14nm CMOS technology and touch upon some systems-level aspects. Finally, I will present some applications of IMC that transcend conventional DL such as memory-augmented neural networks and spiking neural networks.