hardware-aware

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

🚀 Zero-config automatic memory allocator for Rust - just add one line and get up to 1.6x faster allocation performance across all platforms

[SIGMETRICS 2022] One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

pipeDejavu: Hardware-aware Latency Predictable, Differentiable Search for Faster Config and Convergence of Distributed ML Pipeline Parallelism

Optimize memory allocation in Rust with Auto-Allocator. Enjoy smart, platform-aware performance boosts effortlessly. 🌟🚀

Generating neural networks for diverse networking classification tasks via hardware-aware neural architecture search, Transactions on Computers 2023

In this repo you will understand .The process of reducing the precision of a model’s parameters and/or activations (e.g., from 32-bit floating point to 8-bit integers) to make neural networks smaller, faster, and more energy-efficient with minimal accuracy loss.