Pathways to efficient neuromorphic computing with non-volatile memory technologies

Historically, memory technologies have been evaluated based on their storage density, cost, and latencies. Beyond these metrics, the need to enable smarter and intelligent computing platforms at a low area and energy cost has brought forth interesting avenues for exploiting non-volatile memory (NVM) technologies. In this paper, we focus on non-volatile memory technologies and their applications to bio-inspired neuromorphic computing, enabling spike-based machine intelligence. Spiking neural networks (SNNs) based on discrete neuronal “action potentials” are not only bio-fidel but also an attractive candidate to achieve energy-efficiency, as compared to state-of-the-art continuous-valued neural networks. NVMs offer promise for implementing both area- and energy-efficient SNN compute fabrics at almost all levels of hierarchy including devices, circuits, architecture, and algorithms. The intrinsic device physics of NVMs can be leveraged to emulate dynamics of individual neurons and synapses. These devices can be connected in a dense crossbar-like circuit, enabling in-memory, highly parallel dot-product computations required for neural networks. Architecturally, such crossbars can be connected in a distributed manner, bringing in additional system-level parallelism, a radical departure from the conventional von-Neumann architecture. Finally, cross-layer optimization across underlying NVM based hardware and learning algorithms can be exploited for resilience in learning and mitigating hardware inaccuracies. The manuscript starts by introducing both neuromorphic computing requirements and non-volatile memory technologies. Subsequently, we not only provide a review of key works but also carefully scrutinize the challenges and opportunities with respect to various NVM technologies at different levels of abstraction from devices-to-circuit-to-architecture and co-design of hardware and algorithm.