Search

In this paper, we generalize the lightweight-slope piecewise-line range-addressable lookup table for efficient approximate computing in any activation/quantization function, and propose an error-correcting algorithm and circuitry using the AN codes for enhancing reliability. A BLER/SNR simulation proves the SEC/DEC capability of the firstly-presented AN-coded neuron. Comparisons with similar state-of-the-art works show that the proposed technique is the most efficient and error-correctable lookup table for any function in a medium resolution within 8-12 bits.

Acceleration and reliability are two critical issues of artificial intelligent circuits and systems. In this paper we propose a novel structure of deep neural network in testing stage that converts the trained weights to optimized ternary-coded binary. Within each shallow layer a constant-shift sub-layer with almostfree cost is inserted to transfer all multipliers to a carry save adder/subtractor. Then the activation functions are simplified to piecewise lines with nice slopes for calculation by an adder only. Since input-side layers tend to have self-healing ability, only output-side layers are equipped by AN codes that can be encoded and decoded almost without extra cost in our structure. From experiments and evaluations, our structure can have a higher resolution with error-correcting capability and a performance similar to the BNN.