SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision Deep Neural Networks
<|reference_start|>SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision Deep Neural Networks: Ternary Deep Neural Networks (DNN) have shown a large potential for highly energy-constrained systems by virtue of their low power operation (due to ultra-low precision) with only a mild degradation in accuracy. To enable an energy-efficient hardware substrate for such systems, we propose a compute-enabled memory design, referred to as SiTe-CiM, which features computing-in-memory (CiM) of dot products between signed ternary (SiTe) inputs and weights. SiTe CiM is based on cross-coupling of two bit cells to enable CiM of dot products in the signed ternary regime. We explore SiTe CiM with 8T-SRAM, 3T-embedded DRAM (3T-eDRAM) and 3T-ferroelectric metal FET (FEMFET) memories. We propose two flavors of this technique, namely SiTe CiM I/II. In SiTe CiM I, we employ two additional transistors per cell for cross-coupling, achieving fast CiM operations, albeit incurring an area overhead ranging from 18% to 34% (compared to standard ternary memories). In SiTe CiM II, four extra transistors are utilized for every 16 cells in a column, thereby incurring only 6% area cost (but leading to slower CiM than SiTe CiM I). Based on the array analysis, our designs achieve up to 88% lower CiM latency and 78% CiM energy savings across various technologies considered, as compared to their respective near-memory computing counterparts. Further, we perform system level analysis by incorporating SiTe CiM I/II arrays in a ternary DNN accelerator and show up to 7X throughput boost and up to 2.5X energy reduction compared to the near-memory ternary DNN accelerators.<|reference_end|>
@article{thakuria2024site,
    title={SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision
  Deep Neural Networks},
    author={Niharika Thakuria, Akul Malhotra, Sandeep K. Thirumala, Reena
  Elangovan, Anand Raghunathan and Sumeet K. Gupta},
    journal={arXiv preprint arXiv:2408.13617},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13617},
    primaryClass={cs.AR}
}