High-Performance Mixed-Signal Neurocomputing With Nanoscale Floating-Gate Memory Cell Arrays.

Abstract

Potential advantages of analog- and mixed-signal nanoelectronic circuits, based on floating-gate devices with adjustable conductance, for neuromorphic computing had been realized long time ago. However, practical realizations of this approach suffered from using rudimentary floating-gate cells of relatively large area. Here, we report a prototype $28\times28$ binary-input, ten-output, three-layer neuromorphic network based on arrays of highly optimized embedded nonvolatile floating-gate cells, redesigned from a commercial 180-nm nor flash memory. All active blocks of the circuit, including 101 780 floating-gate cells, have a total area below 1 mm2. The network has shown a 94.7% classification fidelity on the common Modified National Institute of Standards and Technology benchmark, close to the 96.2% obtained in simulation. The classification of one pattern takes a sub-1- $\mu \text{s}$ time and a sub-20-nJ energy-both numbers much better than in the best reported digital implementations of the same task. Estimates show that a straightforward optimization of the hardware and its transfer to the already available 55-nm technology may increase this advantage to more than $10^{2}\times $ in speed and $10^{4}\times $ in energy efficiency.