Raziel Alvarez

Founded PyTorch's Executorch.

An end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of various PyTorch models (vision, speech, Generative AI, and more) to edge devices.

Selected work I was involved in:

• 📰 Redefined the strategy for PyTorch's own on-device ML suite: "PyTorch Edge"
• 📰 Created the vision and strategy for ExecuTorch
• 📰 Defined the ExecuTorch technical architecture
• 📰 Defined the relationship and integration of PyTorch Core, Intermediate Representations, the new 2.0 APIs and ExecuTorch's
• 📰 Key contributor to define PyTorch's torch.export IR
• 📰 Key contributor to define PyTorch's EXIR dialects
• 📰 Redefined the strategy and architecture ot PyTorch's Architecture Optimization and ML optimization around torch.export
• 📰 Worked on the first embedded use of ExecuTorch within Meta's smart classes

Founded the TensorFlow Model optimization toolkit.

A suite of tools for optimizing machine learning models for deployment and execution, via easy to use and consistent APIs implementing powerful machine learning optimization techniques.

Selected work I was involved in:

• 📰 Introducing the Model Optimization Toolkit for Tensorflow
• 📰 TensorFlow Model Optimization Toolkit—Pruning API
• 📰 TensorFlow Model Optimization Toolkit—Post-Training Integer Quantization - blog
• 📰 TensorFlow Model Optimization Toolkit—Post-training reduced-precision fp16 quantization
• 📰 TensorFlow Model Optimization Toolkit—Quantization Aware Training API
• 📰 EfficientNet-EdgeTPU: Accelerator-aware neural network design with AutoML
• 📰 Coral summer updates: Post-training quant support, TF Lite delegate, and new models!
• 📰 TensorFlow Model Optimization Toolkit — Weight Clustering API

Co-Founded TensorFlow Lite.

Google's open source deep learning framework for on-device machine learning. It has billions of installs, from mobile phones, smart displays and speakers, to cars and wearables, powering Google's and other companies products.

Selected work I was involved in:

• 📰 Announcing TensorFlow Lite
• 📰 TensorFlow operation fusion in the TensorFlow Lite converter
• 📰 ML Kit expands into NLP with Language Identification and Smart Reply
• 📰 Higher accuracy on vision models with EfficientNet-Lite
• 📰 Accelerating TensorFlow Lite with XNNPACK Integration

Google On-device speech recognition

I worked in the Google speech team to bring speech and related technologies to work entirely on-device. I was part of the team that developed the very-low-power "hey Google" capabilities across devices, where I developed the first end-to-end system (and latest iteration of the ML model). I also built the pre-TensorFlow ML inference engine that brought a new generation of speech recognizers, text-to-speech generators, and keyboard technology on-device.

This infrastructure, along with many techniques I implemented, promoted and refined --such as quantization, sparsity, and the replacement of vanilla LSTMs for Coupled Input-Forget Gate (CIFG) variant (introduced here)-- became the foudation of newer infrastructure and production models.