CafeHD: A Charge-Domain FeFET-Based Compute-in-Memory Hyperdimensional Encoder with Hypervector Merging

Hyperdimensional computing (HDC) is an emerging paradigm that employs hypervectors (HV s) to emulate cognitive tasks. In HDC, the most time-consuming and power-hungry process is encoding, the first step that maps raw data into HV s. There have been non-volatile memory (NVM) based computing-in-memory (CiM) HDC encoding designs, which exploit the intrinsic HDC characteristics of high parallelism, massive data, and robustness. These NVM-based CiMs have shown great potential in reducing encoding time and power consumption. Among them, the ferroelectric field-effect transistor (FeFET) based designs show ultra-high energy efficiency. However, existing FeFET-based HDC encoding designs face the challenges of energy -consuming current-mode addition, inefficient HV storage, limited endurance, and single encoding method support. These challenges limit the energy efficiency, lifetime, and versatility of the designs. This work proposes an energy-efficient charge-domain FeFET-based in-memory HDC encoder, i.e., CafeHD, with extended lifetime, good versatility, and comparable accuracy. Area-efficient charge-domain computing is proposed in HDC encoding for the first time, which enables CafeHD with ultra-low power and high scalability. An HV merging technique is explored to improve the performance. A low-cost partial MAJ interface is also proposed to reduce writes. Besides, CafeHD also supports two widely used encoding methods. Results show that CafeHD on average achieves 10.9×/12.7×/3.5× speedup and 103.3×/21.9×/6.3× energy effi-ciency with ~84 % write times reduction and similar accuracy compared with the state-of-the-art ReRAM/PCMlFeFET-based CiM design for HDC encoding, respectively.