Peat, a Python-based Intel-Optimized Tensorflow dockerization with CPU & Memory constraints configurator
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Updated
Aug 5, 2020 - Python
Peat, a Python-based Intel-Optimized Tensorflow dockerization with CPU & Memory constraints configurator
Hardware accelerated OpenCV, Torch & Tensorrt Ubuntu 20.04 docker images for Jetson Nano containing any python version you need up until the latest 3.12
Deep learning library that exports itself to HDL code for FPGA-based hardware acceleration
Implementation of Random Sparse adaptation using python and tensorflow
Real time, battery powered, Convolutional Neural Net inferencing on the Movidius NCS and a Raspbery Pi using a webcam
Design Space Exploration (DSE) simulator for binary neural network accelerator
A Tool for Parallel Processing of ROS2 Hardware Acceleration on Zynq
Learned Approximate Matrix Profile (LAMP) implementation on Ultra96-v2 board
Scalable linear regression for multi-GPU, TPU training with PyTorch
Running XOR encoder
An extension for colcon-core to include embedded and Hardware Acceleration capabilities
Single Shot MultiBox Detector deployed on a OAK-D Lite cam via DepthAI
Code used in the paper “Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks”
Implementation of a compact optical neural network SqueezeLight based on multi-operand micro-rings, DATE 2021
An open-source parameterizable NPU generator with full-stack multi-target compilation stack for intelligent workloads.
Github page for SSDFA
MobileNet trained with VoxCeleb dataset and used for voice verification
Open source RTL simulation acceleration on commodity hardware
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