Jason Knight

Jason Knight

Feb 25, 2021

Jason Knight

Jason Knight

Feb 25, 2021

Compiling classical ML for performance gains (up to 30x) and hardware portability

Today, machine learning engineers and data scientists use popular frameworks such as Scikit-learn, XGBoost, and LightGBM to train and deploy classical ML models such as linear and logistic regression, decision trees and gradient boosting.

Jason Knight

Jason Knight

Jan 15, 2021

Jason Knight

Jason Knight

Jan 15, 2021

In the cloud — Sparsity on GPUs provides 5X speedup

As AI models get larger, the importance of each weight for a typical inferencing decreases...

Sayce Falk

Sayce Falk

Dec 16, 2020

Sayce Falk

Sayce Falk

Dec 16, 2020

On the Apple M1, Beating Apple’s Core ML 4 With 50% Model Performance Improvements

Apple’s release of an Arm-based chip, called the M1, was a seismic shift in the personal computing landscape.

Thierry Moreau

Thierry Moreau

Dec 4, 2020

Thierry Moreau

Thierry Moreau

Dec 4, 2020

Amplify ML Hardware Design Productivity with TVM-driven Hardware Simulation

Machine learning (ML) has spurred tremendous innovation in CPU, GPUs and NPUs.

Sayce Falk

Sayce Falk

Nov 5, 2020

Sayce Falk

Sayce Falk

Nov 5, 2020

Unlocking 10x Performance Improvements on Computer Vision Models

At OctoML, we love working with teams that are changing our world through the application and productization of deep learning models.

Accelerate Performance and Deployment Time