A SPARSE MEMORY ACCESS ARCHITECTURE FOR DIGITAL NEURAL-NETWORK LSIS

A sparse memory access architecture which is proposed to achieve a hig h-computational-speed neural-network LSI is described in detail. This architecture uses two key techniques, compressible synapse-weight neur on calculation and differential neuron operation, to reduce the number of accesses to synapse weight memories and the number of neuron calcu lations without incurring an accuracy penalty. The test chip based on this architecture has 96 parallel data-driven processing units and eno ugh memory for 12,288 synapse weights. In a pattern recognition exampl e, the number of memory accesses and neuron calculations was reduced t o 0.87% that needed in the conventional method and the practical perfo rmance was 18 GCPS. The sparse memory access architecture is also effe ctive when the synapse weights are stored in off-chip memory.