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IANUS: Integrated Accelerator based on NPU-PIM Unified Memory System

Publikováno v: ASPLOS 2024

Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed

Externí odkaz: http://arxiv.org/abs/2410.15008

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Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space

Autor: Cho, Min Woo, Hwang, Seok Hyeon, Jang, Jun-Young, Song, Jin Yeong, Hwang, Sun-kwang, Cha, Kyoung Je, Park, Dong Yong, Song, Kyungjun, Park, Sang Min

Ventilated acoustic resonator(VAR), a type of acoustic metamaterial, emerge as an alternative for sound attenuation in environments that require ventilation, owing to its excellent low-frequency attenuation performance and flexible shape adaptability

Externí odkaz: http://arxiv.org/abs/2408.05917

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Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization

Autor: Lee, Janghwan, Kim, Minsoo, Baek, Seungcheol, Hwang, Seok Joong, Sung, Wonyong, Choi, Jungwook

Large Language Models (LLMs) are proficient in natural language processing tasks, but their deployment is often restricted by extensive parameter sizes and computational demands. This paper focuses on post-training quantization (PTQ) in LLMs, specifi

Externí odkaz: http://arxiv.org/abs/2311.05161

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