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A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor With 8-bit Synaptic Weight On-Chip Learning That Operates in the Continuous-Time Domain

Seiji Uenohara, Kazuyuki Aihara

2022IEEE Access11 citationsDOIOpen Access PDF

Abstract

We present a mixed-signal spiking neural networks processor with 8-bit synaptic weight on-chip learning in 40 nm CMOS that consists of a 10k mixed-signal synapse circuit and 100 analog leaky integrate-and-fire (LIF) neuron circuits. The processor has no clock signal except in peripheral circuits for I/O, and neuron and synapse circuits can operate asynchronously in the continuous-time domain, just like biological neurons. We demonstrate the energy efficiency of 6.24–18.7 TOPS/W in a multitarget spike learning task.

Topics & Concepts

TOPSComputer scienceArtificial neural networkChipSpiking neural networkBit (key)Time domainComputer hardwareSIGNAL (programming language)Domain (mathematical analysis)Real-time computingEmbedded systemArtificial intelligenceTelecommunicationsMathematicsComputer networkComputer visionAzimuthMathematical analysisProgramming languageGeometryAdvanced Memory and Neural ComputingNeuroscience and Neural EngineeringFerroelectric and Negative Capacitance Devices
A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor With 8-bit Synaptic Weight On-Chip Learning That Operates in the Continuous-Time Domain | Litcius