Falcon: A Fused-Layer Accelerator With Layer-Wise Hybrid Inference Flow for Computational Imaging CNNs

Abstract

Computational imaging (CI) has advanced significantly due to the use of convolutional neural networks (CNNs). Its edge deployment relies on layer fusion to offload the monstrous external memory access (EMA) of feature maps, necessitating the handling of overlapped features either through reusing or recomputing them. Depending on how the boundary-handling strategy is organized, the induced computing complexity and EMA can be optimized. However, state-of-the-art CI accelerators primarily apply homogeneous inference flows, which employ a single overlap-handling strategy throughout the fused layers, limiting their ability to balance computation and data access. In this article, we explore layer-wise optimization in fused-layer CNNs by exploiting hybrid-strategy inference flows and devising a corresponding computing architecture. We categorize layer-wise strategies and put forward a layer-wise hybrid inference flow (LHIF) to integrate their advantages, and we propose an optimization procedure that explicitly analyzes essential figures of merit (FoMs), including throughput, EMA, and energy efficiency. Furthermore, we develop a high-throughput accelerator—Falcon—to efficiently support LHIF under massive parallelism, especially with a time-division-multiplexing (TDM) buffer interface that enables seamless access to feature maps stored in an interleaved manner. Layout results show that the accelerator, delivering 41 TOPS with 1.5 MB of feature-map buffers, supports LHIF while increasing the die area by only 1.4% and power consumption by only 0.7%. Extensive simulations are conducted to demonstrate the versatility of LHIF in working scenarios at operational, design, and system levels. Compared with using homogeneous inference flows, the proposed LHIF achieves Pareto optimality with up to $2.28\times $ higher throughput and $3.5\times $ lower EMA.