Nature research paper: A compute-in-memory chip based on resistive random-access memory
). When selecting the matrices to merge, we want to avoid the matrices that belong to the same two categories described in the previous paragraph: those that have high computational intensity to minimize impact on throughput; and those with ‘wide’ output dimension to avoid a large voltage drop.
To enable fast implementation of various machine-learning applications on the NeuRRAM chip, we developed a software toolchain that provides Python-based application programming interfaces at various levels.
To measure the input-stage energy efficiency, we generate a 256 × 256 random weight matrix with Gaussian distribution, split it into 2 segments, each with dimension 128 × 256, and program the two segments to two cores using the differential-row weight mapping. We measure the power consumption and latency for performing 10 million MVMs, or equivalently 655 billion MAC operations. The comparison with previous work shown in Fig.
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