Neural network computations on a single chip are largely constrained by power and memory bandwidth. To achieve higher degrees of throughput for neural network workloads, in addition to the above memory innovations, we have invented a new numeric format called Flexpoint. Flexpoint allows scalar computations to be implemented as fixed-point multiplications and additions while allowing for large dynamic range using a shared exponent. Since each circuit is smaller, this results in a vast increase in parallelism on a die while simultaneously decreasing power per computation.
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