DPE-CIM: Compute-In-Memory Accelerator using Dynamic Posit Encoding and Speculative Alignment

In this study, we propose two novel approaches to address the memory wall of AI accelerators. First, based on Posit, we introduce a new format called dynamic Posit encoding (DPE), which dynamically extends the dynamic range of its representation at run time with minimal hardware overhead. Using two exponent encoding schemes, DPE accommodates the data distribution with lower quantization error compared to regular Posit. Second, we propose a compute-in-memory (CIM) architecture to implement DPE multiply-and-accumulate (MAC) computation to reduce weight data movement. Traditional CIM proposed for floating-point-alike MAC computation uses a comparator tree (CT) to compute the maximum exponent, enabling the CIM to locus on integer MAC. However, the CT-based design has poor scalability as the number of inputs increases. To address this, we propose a speculative input alignment design that significantly reduces the delay, area, and power consumption for the max exponent computation. We show that DPE outperforms state-of-the-art quantization approaches across various neural network models through software evaluations. Hardware synthesis and simulation results further illustrate that our approach achieves significant energy efficiency and area efficiency improvement compared to the state-of-the-art posit processing element.