Abu Sebastian Research Staff Member IBM Research - Zurich Switzerland 8 (Africa, Europe, Middle East) Email 2022 2023 Talk(s): In-Memory Computing for Deep Learning and Beyond 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. In-Memory Computing: Memory Devices and Applications In-Memory Computing: Memory Devices and Applications × Traditional computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. In this lecture, I will give an overview of the primary charge-based and resistance-based Seminar Titles (Maximum 3) memory devices being explored for in-memory computing as well as the key computational primitives they enable. Subsequently, I will present an overview of the key applications of in-memory computing that span scientific computing, signal processing, optimization, machine learning, deep learning, and stochastic computing.
