6 papers
PolySim: Deterministic Polynomial Surrogates for Cross-Modal Retrieval on CiM
Xinzhao Li, Charles Power, Pengyu Ren +10
Cross-modal retrieval on edge devices benefits from probabilistic embeddings that capture semantic uncertainty, but deploying them on compute-in-memory (CiM) hardware remains an op…
CQ-CiM: Hardware-Aware Embedding Shaping for Robust CiM-Based Retrieval
Xinzhao Li, Alptekin Vardar, Franz Müller +6
Deploying Retrieval-Augmented Generation (RAG) on edge devices is in high demand, but is hindered by the latency of massive data movement and computation on traditional architectur…
When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators
Yifan Qin, Jiahao Zheng, Zheyu Yan +3
Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottl…
Secure Scattered Memory: Rethinking Secure Enclave Memory with Secret Sharing
Haoran Geng, Yuezhi Che, Dazhao Chen +2
The rise of cloud computing demands secure memory systems that ensure data confidentiality, integrity, and freshness against replay attacks. Existing schemes such as AES-XTS, AES-G…
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…
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…