  Recommendation systems (RecSys) suggest items to users by predicting their
preferences based on historical data. Typical RecSys handle large embedding
tables and many embedding table related operations. The memory size and
bandwidth of the conventional computer architecture restrict the performance of
RecSys. This work proposes an in-memory-computing (IMC) architecture (iMARS)
for accelerating the filtering and ranking stages of deep neural network-based
RecSys. iMARS leverages IMC-friendly embedding tables implemented inside a
ferroelectric FET based IMC fabric. Circuit-level and system-level evaluation
show that \fw achieves 16.8x (713x) end-to-end latency (energy) improvement
compared to the GPU counterpart for the MovieLens dataset.
