A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm

TLDR

Neuromorphic computing aims to create flexible, brain‑like systems that support many real‑time applications while matching the human brain’s ultra‑low power and compactness within current silicon limits. The authors fabricated a neurosynaptic core comprising 256 digital integrate‑and‑fire neurons and a 1024×256‑bit SRAM crossbar for synapses in IBM’s 45 nm SOI process. The core is a fully digital, one‑to‑one hardware‑software design that tightly couples 256 neurons with a 1024×256‑bit SRAM crossbar, enabling efficient parallel, event‑driven communication and 45 pJ per spike while remaining fully configurable. An offline‑trained restricted Boltzmann machine for visual digit recognition was mapped onto the chip, demonstrating functional deployment.

Abstract

The grand challenge of neuromorphic computation is to develop a flexible brain-like architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of the human brain-within the constraints of existing silicon and post-silicon technologies. To this end, we fabricated a key building block of a modular neuromorphic architecture, a neurosynaptic core, with 256 digital integrate-and-fire neurons and a 1024×256 bit SRAM crossbar memory for synapses using IBM's 45nm SOI process. Our fully digital implementation is able to leverage favorable CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. In contrast to a conventional von Neumann architecture, our core tightly integrates computation (neurons) alongside memory (synapses), which allows us to implement efficient fan-out (communication) in a naturally parallel and event-driven manner, leading to ultra-low active power consumption of 45pJ/spike. The core is fully configurable in terms of neuron parameters, axon types, and synapse states and is thus amenable to a wide range of applications. As an example, we trained a restricted Boltzmann machine offline to perform a visual digit recognition task, and mapped the learned weights to our chip.