AERO: Design Space Exploration Framework for Resource-Constrained CNN Mapping on Tile-Based Accelerators

Abstract

Analog In-Memory Compute (AIMC) arrays can store weights and perform matrix-vector multiplication operations for Deep Convolutional Neural Networks (CNNs). A number of recent efforts have integrated AIMC arrays into hybrid digital-analog accelerators in a multi-layer parallel manner to achieve energy efficiency and high throughput. Multi-layer parallelism on large-scale tile-based architectures need efficient mapping support at the processing element (PE)-level (e.g., digital or analog processing elements) and tile-level. To find the most efficient architectures, fast and accurate design space exploration (DSE) support is required. In this paper, a novel DSE framework, AERO, is presented to characterize a CNN inference workload executing on hybrid tile-based architectures that supports multi-layer parallelism. Three characteristics can be seen in our DSE framework: (1) It presents a hierarchical Tile/PE-level mapping exploration strategy including inter-layer interaction, and allowing layer fusion/splitting configurations for PE-level mapping optimization. (2) It unlocks different Performance, Power and Area (PPA) exploration points under both sufficient and limited resource constraints, while limited resource case is not considered in prior works of multi-layer parallel architectures. The impact of weight loading and weight stationary mapping are analyzed for better insights into hybrid tile-based architectures. (3) It incorporates a detailed PPA model that supports a broad range of hybrid digital and analog units in a tile. Experimental case-studies are performed for realistic and relevant benchmarks such as MLP, CNNs (Lenet-5, Resnet-18,-34,-50 and -101).