A high-speed analog neural processor

Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight discretization, synapse nonlinearity, noise, and other non-ideal effects. Although our prototype does not take advantage of advanced CMOS technology, and was fabricated using a 2.5-μm CMOS process, it performs 6 billion multiplications per second, with only 2 W dissipation, and has as high as 1.5 Gbyte/s equivalent bandwidth

A high-speed analog neural processor

Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight discretization, synapse nonlinearity, noise, and other non-ideal effects. Although our prototype does not take advantage of advanced CMOS technology, and was fabricated using a 2.5-μm CMOS process, it performs 6 billion multiplications per second, with only 2 W dissipation, and has as high as 1.5 Gbyte/s equivalent bandwidth