184 citations · 338 across the 11 of their papers we have counts for
11 papers
ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device
Mergen Nachin, Digant Desai, Sicheng Stephen Jia +36
Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragmented, often requiring model co…
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting
Haichuan Yang, Zhaojun Yang, Li Wan +10
This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units li…
On the quantization of recurrent neural networks
Jian Li, Raziel Alvarez
Integer quantization of neural networks can be defined as the approximation of the high precision computation of the canonical neural network formulation, using reduced integer pre…
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Tara N. Sainath, Yanzhang He, Bo Li +26
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…
Optimizing Speech Recognition For The Edge
Yuan Shangguan, Jian Li, Qiao Liang +2
While most deployed speech recognition systems today still run on servers, we are in the midst of a transition towards deployments on edge devices. This leap to the edge is powered…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…