Summary of Nitro-d: Native Integer-only Training Of Deep Convolutional Neural Networks, by Alberto Pirillo et al.
NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networksby Alberto Pirillo, Luca Colombo, Manuel RoveriFirst…
Summary of Quantum Maximum Entropy Inference and Hamiltonian Learning, by Minbo Gao et al.
Quantum Maximum Entropy Inference and Hamiltonian Learningby Minbo Gao, Zhengfeng Ji, Fuchao WeiFirst submitted to…
Summary of Self-guided Generation Of Minority Samples Using Diffusion Models, by Soobin Um and Jong Chul Ye
Self-Guided Generation of Minority Samples Using Diffusion Modelsby Soobin Um, Jong Chul YeFirst submitted to…
Summary of Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations Among Multivariates, by Gang Su et al.
Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariatesby Gang Su, Sun…
Summary of Knowledge Boosting During Low-latency Inference, by Vidya Srinivas et al.
Knowledge boosting during low-latency inferenceby Vidya Srinivas, Malek Itani, Tuochao Chen, Sefik Emre Eskimez, Takuya…
Summary of Exploring the Boundaries Of On-device Inference: When Tiny Falls Short, Go Hierarchical, by Adarsh Prasad Behera et al.
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchicalby Adarsh Prasad Behera,…
Summary of Fuzzy Recurrent Stochastic Configuration Networks For Industrial Data Analytics, by Dianhui Wang and Gang Dang
Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analyticsby Dianhui Wang, Gang DangFirst submitted to…
Summary of Isquant: Apply Squant to the Real Deployment, by Dezan Zhao
ISQuant: apply squant to the real deploymentby Dezan ZhaoFirst submitted to arxiv on: 5 Jul…
Summary of Integer-only Quantized Transformers For Embedded Fpga-based Time-series Forecasting in Aiot, by Tianheng Ling et al.
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoTby Tianheng Ling, Chao Qian, Gregor…
Summary of Q-sparse: All Large Language Models Can Be Fully Sparsely-activated, by Hongyu Wang et al.
Q-Sparse: All Large Language Models can be Fully Sparsely-Activatedby Hongyu Wang, Shuming Ma, Ruiping Wang,…