Skip to Main Content
A high-performance, general-purpose neuro-computer composed of 512 digital neurons is developed. Each neuron has an execution unit which is optimized for traditional neural functions, but the use of a micro-programming architecture makes it general enough to implement any neural function. Horizontal micro-instruction formats and massively parallel-pipelined computation allows high-speed on-chip learning. The theoretical maximum learning speed for the backpropagation algorithm is 1.25 GCUPS (giga connection updates per second). Eight digital neurons are integrated on each neuron chip by using 1.0-μm CMOS technology, and 64 neuron chips are packaged in this hardware. This hardware can be connected to a host workstation by a SCSI network. We applied this neuro-computer to handwritten numerals recognition. The learning speed by using the neuro-computer is over 1000 times faster than by using the workstation.