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Found 6 papers, 2 papers with code
QGen: On the Ability to Generalize in Quantization Aware Training
In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance.
DeepliteRT: Computer Vision at the Edge
The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications.
Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity
The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment.
YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems
We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia GPU, NPU).
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables
A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices.
Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtime
Deep Learning has been one of the most disruptive technological advancements in recent times.
