Results 151 to 160 of about 249,885 (208)

Chenodeoxycholic acid triggers gastric mucosal injury by inducing apoptosis and FXR activation. [PDF]

PLoS One
Peng S, Sun R, Liu H, Wang B, Li Y.
europepmc   +1 more source

Perception-Based H.264/AVC Video Coding for Resource-Constrained and Low-Bit-Rate Applications. [PDF]

Sensors (Basel)
Kau LJ, Tseng CK, Lee MX.
europepmc   +1 more source

An empirical study of LLaMA3 quantization: from LLMs to MLLMs. [PDF]

Vis Intell
Huang W, Zheng X, Ma X, Qin H, Lv C, Chen H, Luo J, Qi X, Liu X, Magno M.   +9 more
europepmc   +1 more source

TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing. [PDF]

Sci Rep
Khatoon A, Wang W, Wang M, Li L, Ullah A.   +4 more
europepmc   +1 more source

Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications. [PDF]

Sensors (Basel)
Hizem M, Bousbia L, Ben Dhiab Y, Aoueileyine MO, Bouallegue R.   +4 more
europepmc   +1 more source

FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV Environments. [PDF]

Sensors (Basel)
Ha J, El Azzaoui A, Park JH.
europepmc   +1 more source

Lossless and Near-Lossless L-Infinite Compression of Depth Video Data. [PDF]

Sensors (Basel)
Tahouri MA, Alecu AA, Denis L, Munteanu A.   +3 more
europepmc   +1 more source

The Dynamical Evolution Parameter in Manifestly Covariant Quantum Gravity Theory. [PDF]

Entropy (Basel)
Cremaschini C.
europepmc   +1 more source

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AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration

Conference on Machine Learning and Systems, 2023
Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce cloud computing costs and protect users' privacy.
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, Song Han   +5 more
semanticscholar   +1 more source

Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Conference on Machine Learning and Systems, 2023
The growing demand for Large Language Models (LLMs) in applications such as content generation, intelligent chatbots, and sentiment analysis poses considerable challenges for LLM service providers.
Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye, Lequn Chen, Size Zheng, Luis Ceze, Arvind Krishnamurthy, Tianqi Chen, Baris Kasikci   +9 more
semanticscholar   +1 more source