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From the 1 of 8 linked papers with an AI index.

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8 papers

cs.AR2026

At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference

Bowen Wang, Chi Zhang, Diyou Shen +3

The paper introduces Ventaglio, a hardware extension and ISA support for vector processors that efficiently executes sparse tensor contractions in Transformer inference, achieving…

cs.AR2026

Accelerating Precise End-to-End Simulation: Latency-Sensitive Many-core System Modeling

Yinrong Li, Zexin Fu, Yichao Zhang +5

Modern large language model workloads put increasing demands on parallel compute capability and on-chip memory capacity, while also stressing fine-grained data movement and synchro…

cs.AR2026

FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Large Attention-Based Model Inference on Tile-Based Accelerators

Chi Zhang, Luca Colagrande, Renzo Andri +1

Attention accounts for an increasingly dominant fraction of total computation during inference for mixture-of-experts (MoE) models, making efficient acceleration critical. Emerging…

cs.DC2026

TeraPool: A Physical Design Aware, 1024 RISC-V Cores Shared-L1-Memory Scaled-up Cluster Design with High Bandwidth Main Memory Link

Yichao Zhang, Marco Bertuletti, Chi Zhang +5

Shared L1-memory clusters of streamlined instruction processors (processing elements - PEs) are commonly used as building blocks in modern, massively parallel computing architectur…

cs.DC2025

Design in Tiles: Automating GEMM Deployment on Tile-Based Many-PE Accelerators

Aofeng Shen, Chi Zhang, Yakup Budanaz +4

Tile-based many-Processing Element (PE) accelerators can achieve competitive performance on General Matrix Multiplication (GEMM), but they are extremely hard to program, as their o…

cs.LG2025

VLM in a flash: I/O-Efficient Sparsification of Vision-Language Model via Neuron Chunking

Kichang Yang, Seonjun Kim, Minjae Kim +3

Edge deployment of large Vision-Language Models (VLMs) increasingly relies on flash-based weight offloading, where activation sparsification is used to reduce I/O overhead. However…