Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded NOR flash memory technology

Abstract

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We have designed, fabricated, and tested a prototype mixed-signal, 28×28-binary-input, 10-ouput, 3-layer neuromorphic network based on embedded nonvolatile floating-gate cell arrays redesigned from a commercial 180-nm NOR flash memory. Each array performs a very fast and energy-efficient analog vector-by-matrix multiplication, which is the bottleneck for signal propagation in neuromorphic networks. All functional components of the prototype circuit, including 2 synaptic arrays with 101,780 floating-gate synaptic cells, 74 analog neurons, and the peripheral circuitry for weight adjustment and I/O operations, have a total area below 1 mm2. Its testing on the MNIST benchmark set has shown a classification fidelity of 94.65%, close to the 96.2% obtained in simulation. The classification of one pattern takes 103× better than those of the 28-nm IBM TrueNorth digital chip for the same task at a similar fidelity. Estimates show that this performance may be further improved using a better neuron design and a more advanced memory technology, leading to a >102x advantage in speed and a >104x advantage in energy efficiency over the state-of-the-art purely digital circuits for classification of large, complex patterns. Experimental results for the chip-to-chip statistics, long-term drift, and temperature sensitivity show no evident showstoppers on the way toward practical deep neuromorphic networks with unprecedented performance.