Presentation
High Performance Computing Enabled Through Neuromorphic Systems and In-Memory Computing Primitives
DescriptionWith the rise of data driven workloads and increase in model sizes, it has become increasingly critical to build computationally efficient hardware. Drawing inspiration from the brain to harness the computational advantages of such a neural structure, fundamental blocks of neurons and synapses are built to implement neuromorphic systems. We discuss techniques to demonstrate energy-efficient computing on analog configurable platforms to enable real-time systems on hardware. Further, we show tree-based machine learning models through In-Memory Computing (IMC) primitives such as Analog Content Addressable Memories (ACAMs) that are designed with emerging non-volatile technologies such as memristors. Such systems ultimately pave the path to take physical approaches to build large-scale systems for high performance computing in a holistic manner.