Energy-Efficient, Low-Latency Realization of Neural Networks Through Boolean Logic Minimization

Sponsor: TBD

To cope with computational and storage complexity of deep neural networks, this project focuses on a training method that enables a radically different approach for realization of deep neural networks through Boolean logic minimization. The aforementioned realization completely removes the energy-hungry step of accessing memory for obtaining model parameters, consumes about two orders of magnitude fewer computing resources compared to realizations that use floating-point operations, and has a substantially lower latency.Related work: