MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing [article]

Arman Kazemi, Mohammad Mehdi Sharifi, Zhuowen Zou, Michael Niemier, X. Sharon Hu, Mohsen Imani
2021 arXiv   pre-print
Hyperdimensional Computing (HDC) is an emerging computational framework that mimics important brain functions by operating over high-dimensional vectors, called hypervectors (HVs). In-memory computing implementations of HDC are desirable since they can significantly reduce data transfer overheads. All existing in-memory HDC platforms consider binary HVs where each dimension is represented with a single bit. However, utilizing multi-bit HVs allows HDC to achieve acceptable accuracies in lower
more » ... ensions which in turn leads to higher energy efficiencies. Thus, we propose a highly accurate and efficient multi-bit in-memory HDC inference platform called MIMHD. MIMHD supports multi-bit operations using ferroelectric field-effect transistor (FeFET) crossbar arrays for multiply-and-add and FeFET multi-bit content-addressable memories for associative search. We also introduce a novel hardware-aware retraining framework (HWART) that trains the HDC model to learn to work with MIMHD. For six popular datasets and 4000 dimension HVs, MIMHD using 3-bit (2-bit) precision HVs achieves (i) average accuracies of 92.6 (4.8 improvement over a GPU, and (iii) 38.4x (34.3x) speedup over a GPU, respectively. The 3-bit × is 4.3x and 13x faster and more energy-efficient than binary HDC accelerators while achieving similar accuracies.
arXiv:2106.12029v1 fatcat:npmwowsm2zhzpe24ue63crhucm