A Design Framework of Heterogeneous Approximate DCIM-Based Accelerator for Energy-Efficient NN Processing

Static random-access memory (SRAM) based digital compute-in-memory (DCIM) provides error-resilient computation at the expense of considerable power overhead of adder tree. In recent works, DCIM macro based on approximate computing mitigates the adder tree overheads, however, it faces a trade-off between power and neural network (NN) accuracy. The trade-off becomes more complicated in array-level CIM architecture since output channels of NN model have different sensitivities to approximation errors. In this paper, we propose a heterogeneous approximate DCIM-based accelerator design framework that achieves a good energy-accuracy trade-off for a specific NN model. The framework includes three key features: 1) Evolutionary algorithm-based search finds cost-efficient approximation points by pruning the design space. 2) Genetic algorithm-based channel-wise mapping creates heterogeneous approximation methods that effectively reduce DCIM energy consumption while maintaining high accuracy. 3) A hardware generation strategy decides the number of DCIM macros and their sizes, resulting in an energy-efficient DCIM-based accelerator tailored for the given NN model. Experimental results show that employing the proposed heterogeneous channel-wise mapping significantly enhances the energy efficiency compared to a homogeneous mapping. Moreover, the proposed framework can produce heterogeneous DCIM-based accelerators that consume less energy than state-of-the-art approximate DCIM approaches.