Namespace:    kube-environment

PI: Bill Lin
Institution: University of California, San Diego
Project description:

Recent works have demonstrated the promise of using resistive random access memory (ReRAM) to perform neural network computations in memory. In particular, ReRAM-based crossbar structures can perform matrix-vector multiplication directly in the analog domain, but the resolutions of ReRAM cells and digital/ analog converters limit the precisions of inputs and weights that can be directly supported. In this project, we are developing a new CNN training and implementation approach that implements weights using a trained biased number representation, which can achieve near full-precision model accuracy with as little as 2-bit weights and 2-bit activations..

Software: PyTorch, Python, CUDA

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