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pro vyhledávání: '"FANG, Chao"'
The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage requirements while maintaining accuracy. However, this shifts the energy and
Externí odkaz:
http://arxiv.org/abs/2411.15982
In this paper, we investigate the problem of jamming detection and channel estimation during multi-user uplink beam training under random pilot jamming attacks in beamspace massive multi-input-multi-output (MIMO) systems. For jamming detection, we di
Externí odkaz:
http://arxiv.org/abs/2410.14215
Large language models (LLMs) have been widely applied but face challenges in efficient inference. While quantization methods reduce computational demands, ultra-low bit quantization with arbitrary precision is hindered by limited GPU Tensor Core supp
Externí odkaz:
http://arxiv.org/abs/2409.17870
Deploying deep neural networks (DNNs) on those resource-constrained edge platforms is hindered by their substantial computation and storage demands. Quantized multi-precision DNNs, denoted as MP-DNNs, offer a promising solution for these limitations
Externí odkaz:
http://arxiv.org/abs/2409.14017
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment
Transformer models have revolutionized AI tasks, but their large size hinders real-world deployment on resource-constrained and latency-critical edge devices. While binarized Transformers offer a promising solution by significantly reducing model siz
Externí odkaz:
http://arxiv.org/abs/2407.12070
Autor:
Fang, Chao
Exploring unconventional hydrocarbon reservoirs and enhancing the recovery of hydrocarbon from conventional reservoirs are necessary for meeting the society's ever-increasing energy demand and requires a thorough understanding of the multiphase inter
Externí odkaz:
http://hdl.handle.net/10919/89911
RISC-V processors encounter substantial challenges in deploying multi-precision deep neural networks (DNNs) due to their restricted precision support, constrained throughput, and suboptimal dataflow design. To tackle these challenges, a scalable RISC
Externí odkaz:
http://arxiv.org/abs/2401.16872
Existing binary Transformers are promising in edge deployment due to their compact model size, low computational complexity, and considerable inference accuracy. However, deploying binary Transformers faces challenges on prior processors due to ineff
Externí odkaz:
http://arxiv.org/abs/2401.11851
We investigate the ground-state probabilistic logic based on a binary energy landscape (GSPL-BEL) model, implementing the many-body interactions within Ising model cells. The GSPL-BEL model offers a simplified binary energy landscape, enabling the co
Externí odkaz:
http://arxiv.org/abs/2311.00410