A VLSI neuroprocessor for image restoration using analog computing-based systolic architecture

Abstract

An analog computing-based systolic architecture which employs multiple neuroprocessors for high-speed early vision processing is presented. For a two-dimensional image, parallel processing is performed in the row direction and pipelined processing is performed in the column direction. The mixed analog/digital design approach is suitable for implementation of electronic neural systems. Local data computation is executed by analog circuitry to achieve full parallelism and to minimize power dissipation. Inter-processor communication is carried out in the digital format to maintain strong signal strength across the chip boundary and to achieve direct scalability in neural network size. For demonstration purposes, a compact and efficient VLSI neural chip that includes multiple neuroprocessors for high-speed digital image restoration is designed. Measured results of the programmable synapse, and statistical distribution of measured synapse conductances are presented. Based on these results, system-level analyses at 8-bit resolution are conducted. A 8.0×6.0-mm2 chip from a 1.2-µm CMOS technology can accommodate 5 neuroprocessors and the speed-up factor over the Sun-4/75 SPARC workstation is around 450. This chip achieves 18 Giga connections per second.