2 citations · 4 across the 6 of their papers we have counts for
6 papers
KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a…
BEOL Ferroelectric Compute-in-Memory Ising Machine for Simulated Bifurcation
Yu Qian, Alptekin Vardar, Konrad Seidel +7
Computationally hard combinatorial optimization problems are pervasive in science and engineering, yet their NP-hard nature renders them increasingly inefficient to solve on conven…
Device-Algorithm Co-Design of Ferroelectric Compute-in-Memory In-Situ Annealer for Combinatorial Optimization Problems
Yu Qian, Xianmin Huang, Ranran Wang +5
Combinatorial optimization problems (COPs) are crucial in many applications but are computationally demanding. Traditional Ising annealers address COPs by directly converting them…
TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory
Chenyu Ni, Sijie Chen, Che-Kai Liu +8
Pattern search is crucial in numerous analytic applications for retrieving data entries akin to the query. Content Addressable Memories (CAMs), an in-memory computing fabric, direc…
Paradigm-Based Automatic HDL Code Generation Using LLMs
Wenhao Sun, Bing Li, Grace Li Zhang +3
While large language models (LLMs) have demonstrated the ability to generate hardware description language (HDL) code for digital circuits, they still face the hallucination proble…
A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI
Xunzhao Yin, Hamza Errahmouni Barkam, Franz Müller +14
Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Comprising…