Jun Sawada

TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.

TL;DR: This work developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture, and successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition.

TL;DR: A simple, digital, reconfigurable, versatile spiking neuron model that supports one-to-one equivalence between hardware and simulation and is implementable using only 1272 ASIC gates is developed.

TL;DR: IBM's brain-inspired processor is a massively parallel neural network inference engine containing 1 million spiking neurons and 256 million low-precision synapses, making it the largest neurosynaptic computer ever built.

TL;DR: In this article, a 32-kb subarray is implemented with a 65 nm node 8T-SRAM cell for variability tolerance in high-speed caches, achieving 5.3 GHz operation at 1.2 V.