2.1k citations · 2.2k across the 10 of their papers we have counts for
5 papers · 1 filter
Efficiently Scaling Transformer Inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery +7
We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long seque…
Streaming Parrotron for on-device speech-to-speech conversion
Oleg Rybakov, Fadi Biadsy, Xia Zhang +3
We present a fully on-device streaming Speech2Speech conversion model that normalizes a given input speech directly to synthesized output speech. Deploying such a model on mobile d…
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
Sheng-Chun Kao, Amir Yazdanbakhsh, Suvinay Subramanian +3
Sparsity has become one of the promising methods to compress and accelerate Deep Neural Networks (DNNs). Among different categories of sparsity, structured sparsity has gained more…
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin +64
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of…
4-bit Conformer with Native Quantization Aware Training for Speech Recognition
Shaojin Ding, Phoenix Meadowlark, Yanzhang He +3
Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model qu…