Key Algorithms of Convolutional Neural Networks and Hardware Implementation of Image Processing

Abstract

Edge computing and artificial intelligence have made the efficient deployment of machine vision algorithms on low-power hardware a critical challenge for integrated circuit design. Given data-intensive image pixels and deep neural network tensors, traditional von Neumann architectures inevitably encounter severe memory and power wall bottlenecks. This paper reviews low-level hardware acceleration strategies to deconstruct the mapping from algorithm logic to silicon substrates. Through systematic analysis, physical dimensionality reduction and operator substitution effectively reduce computational complexity and enhance energy efficiency. For visual preprocessing, line-buffer topologies and multiplier-less shift-and-add designs optimize bandwidth and eliminate DSP dependencies for certain edge detectors. In edge-based inference, 8-bit fixed-point quantization slashes system bus bandwidth demands by over 75% while restricting accuracy degradation to under 0.5%. Concurrently, Winograd fast convolution translates spatial convolutions into transform-domain mappings, substantially reducing core multiplier utilization and driving significant system throughput. Additionally, advanced dataflow scheduling circumvents power-hungry off-chip memory accesses by maximizing on-chip data reuse and resolving dynamic energy crises. Ultimately, these findings provide strong guidelines for the hardware-software co-design of emerging ultra-low power edge Artificial Intelligence (AI) chips.