Autoregressive decoding of large language models (LLMs) on edge devices is dominated by memory-bound general matrix--vector multiplication (GeMV) operations. Processing-using-DRAM (PUD) is a promising computing paradigm that can alleviate this bottleneck by reducing processor--memory data transfers and enabling massive...
Kevin Xu, Tatsuya Kubo, Daichi Tokuda et al.· 0 citations
This work proposes TRIP-Shapley, a propagation-aware Shapley-inspired surrogate for contribution attribution in P2PFL with a model-free coordinator used solely for auditing, and introduces lightweight mechanisms to detect and mitigate misreporting attacks.
Honoka Anada, Tatsuya Kaneko, Ya-Fei Ou et al.· 0 citations
Tenstorrent Blackhole combines distributed local memories, explicit inter-core communication, and Tensix cores decoupling data movement from computation. CUDA offers a substantial HPC software base but leaves physical data placement and scheduling largely implicit. Migrating CUDA HPC kernels to Blackhole requires spati...
Federated learning (FL) enables collaborative training without directly sharing raw data, but remains vulnerable to malicious clients. Voting-based FL improves robustness by partitioning clients into groups, training one model per group, and aggregating predictions by plurality voting. However, under class-disjoint non...
M. Nakada, Honoka Anada, Tatsuya Kaneko et al.· 0 citations
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