TY  - EJOUR
AU  - Manea, Paul-Philipp
AU  - Leroux, Nathan
AU  - Neftci, Emre
AU  - Strachan, John Paul
TI  - Gain Cell-Based Analog Content Addressable Memory for Dynamic Associative tasks in AI
PB  - arXiv
M1  - FZJ-2025-01111
PY  - 2024
AB  - Analog Content Addressable Memories (aCAMs) have proven useful for associative in-memory computing applications like Decision Trees, Finite State Machines, and Hyper-dimensional Computing. While non-volatile implementations using FeFETs and ReRAM devices offer speed, power, and area advantages, they suffer from slow write speeds and limited write cycles, making them less suitable for computations involving fully dynamic data patterns. To address these limitations, in this work, we propose a capacitor gain cell-based aCAM designed for dynamic processing, where frequent memory updates are required. Our system compares analog input voltages to boundaries stored in capacitors, enabling efficient dynamic tasks. We demonstrate the application of aCAM within transformer attention mechanisms by replacing the softmax-scaled dot-product similarity with aCAM similarity, achieving competitive results. Circuit simulations on a TSMC 28 nm node show promising performance in terms of energy efficiency, precision, and latency, making it well-suited for fast, dynamic AI applications.
KW  - Emerging Technologies (cs.ET) (Other)
KW  - FOS: Computer and information sciences (Other)
LB  - PUB:(DE-HGF)25
DO  - DOI:10.48550/arXiv.2410.09755
UR  - https://juser.fz-juelich.de/record/1038062
ER  -