CINEMA: A Configurable Binary Segmentation Based Arithmetic Module for Mixed-Precision In-Memory Acceleration

The emergence of mixed-precision quantization (MPQ) applied to edge AI models highlights the critical need for hardware support. It is a promising model compression approach with minimal accuracy loss but poses a notable hardware design challenge in the intricate balance required between computing reconfigurability and the resulting area or energy overheads. As the Compute-in-Memory (CiM) paradigm becomes prevalent for accelerating edge inference and demonstrates promising results in enhancing energy efficiency by eliminating overloaded data traffic, efficient in-memory computing circuitry becomes paramount to maximize these advantages. While the memory cell itself cannot handle complex arithmetic logic, especially in the context of MPQ-supporting mult-precision computing, the peripheral pairing module is required to be modular, portable, and scalable. In this paper, we propose a novel bit-precision configurable arithmetic module based on binary segmentation, supporting fine-grained precision ranging from 2 to 8 bits. This module achieves a peak throughput of 16 GOPS and maximum energy efficiency of 8.56 TOPS/W, occupying only 1778 μm
²
in 28nm technology node. The throughput and energy efficiency achieve 1.18x and 6.58x improvements compared with baseline work. Its portability enables seamless integration with various memory technologies, enhancing support for MPQ efficiently.