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