169 citations · 187 across the 5 of their papers we have counts for
5 papers
Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput
Arissa Wongpanich, Tayo Oguntebi, Jose Baiocchi Paredes +6
Recent years have seen the emergence of machine learning (ML) workloads deployed in warehouse-scale computing (WSC) settings, also known as ML fleets. As the computational demands…
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang +7
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution…
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models
Xiaofan Zhang, Zongwei Zhou, Deming Chen +1
Recently, large pre-trained models have significantly improved the performance of various Natural LanguageProcessing (NLP) tasks but they are expensive to serve due to long serving…
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Nan Du, Yanping Huang, Andrew M. Dai +24
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to ach…
Exploring the limits of Concurrency in ML Training on Google TPUs
Sameer Kumar, James Bradbury, Cliff Young +16
Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run.…