Auto-Differentiated Fixed Point Notation on Low-Powered Hardware Acceleration

Using less electric power or speeding up processing is catching the interests of researchers in deep learning. Quanti-zation has offered distillation mechanisms that substitute floating numbers for integers, but little been suggested about themselves. The use Q-format notation reduces computational overheads frees resources in-troduction more operations. Our experiments, conditioned on varying regimes, introduce automatic differentiation algo-rithms like fast Fourier transforms and Winograd minimal filtering to reduce complexity (expressed total number MACs) suggest a path towards assistive intelligence concept. Empirical results show that, under specific heuristics, can overcome shortfalls numbers, especially embedded systems. Further benchmarks FPBench standard give details by comparing our proposals with common learning