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Kai Ni

4 papers hereh-index 223 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.ET2
  • cs.LG2
same name
  • Kai Ni — 12 papers, h 6
  • Kai Ni — 6 papers, h 3
  • Kai Ni — 4 papers, h 3
  • Kai Ni — 3 papers, h 12
  • Kai Ni — 3 papers, h 4
  • Kai Ni — 2 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedRobust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.ET2026

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…

cs.ET2026

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…

cs.LG2024

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs

Ruiyang Qin, Pengyu Ren, Zheyu Yan +7

Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource con…

cs.LG2024★ 1 cited

Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures

Ruiyang Qin, Zheyu Yan, Dewen Zeng +8

Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and updating a certain portion of their parameters. Although such learning methods can be optimized…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.