You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Cheng-En Wu, Yi-Ming Chan and Chu-Song Chen "On Merging MobileNets for Efficient Multitask Inference", International Symposium on High-Performance Computer Architecture(HPCA) on Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications(EMC2), 2019
A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation for Efficient Hardware Acceleration on Edge Devices
Dive into the forefront of Large Language Models (LLMs) with our concise guide on the top 10 hot topics. Explore bias mitigation, efficient training, multimodal models, and more. Stay abreast of the latest advancements shaping the landscape of LLMs.
Inference speed / accuracy tradeoff on text classification with transformer models such as BERT, RoBERTa, DeBERTa, SqueezeBERT, MobileBERT, Funnel Transformer, etc.
Exploring Variational Deep Q Networks. A study undertaken for the University of Cambridge's R244 Computer Science Masters Course. Inspired by https://arxiv.org/abs/1711.11225/.
Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'