Directed Accumulator

Directed accumulators are computational methods that efficiently manage the summation of numerical sequences, particularly within the context of deep learning and signal processing. Current research focuses on optimizing accumulator precision for improved efficiency and accuracy in neural network training and inference, exploring techniques like quantization-aware training and post-training quantization, as well as novel accumulator architectures such as exponent-indexed accumulators. These advancements aim to reduce computational costs and improve resource utilization in various applications, including image processing, natural language processing, and medical image analysis, by enabling the use of lower-precision arithmetic without significant performance loss.