A VLSI PROCESSOR ARCHITECTURE FOR A BACKPROPAGATION ACCELERATOR

This paper describes a VLSI processor architecture designed for a back-propagation accelerator. Three techniques are used to accelerate the simulation. The first is a multi-processor approach where a neural network simulation is suitable for parallel processing. By constructing a ring network using several processors, the simulation speed is multiplied by the number of the processors. The second technique is internal parallel processing. Each processor contains 4 multipliers and 4 ALUs that all work in parallel. The third technique is pipelining. The connections of eight functional units change according to the current stage of the back-propagation algorithm. Intermediate data is sent from one functional unit to another without being stored in extra registers and data is processed in a pipeline manner. The data is in 24-bit floating point format (18-bit mantissa and 6-bit exponent). The chip has about 88,000 gates, including microcode ROM for processor control, the processor is designed using 0.8-mum CMOS gate arrays, and the estimated performance at 40 MHz is 20 million connection updates per second (MCUPS). For a ring network with 4 processors, performance can be enhanced up to 90 MCUPS.