2 papers
cs.LG2026
LO-BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference
Reena Elangovan, Charbel Sakr, Anand Raghunathan +1
Post-training quantization (PTQ) is a promising approach to reducing the storage and computational requirements of large language models (LLMs) without additional training cost. Re…
cs.AR2024
SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision Deep Neural Networks
Niharika Thakuria, Akul Malhotra, Sandeep K. Thirumala +3
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…