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Found 16 papers, 6 papers with code
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization levels, and model compression techniques, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models.
Play It Cool: Dynamic Shifting Prevents Thermal Throttling
Machine learning (ML) has entered the mobile era where an enormous number of ML models are deployed on edge devices.
Width Transfer: On the (In)variance of Width Optimization
In this work, we propose width transfer, a technique that harnesses the assumptions that the optimized widths (or channel counts) are regular across sizes and depths.
One Weight Bitwidth to Rule Them All
Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applications where memory storage is limited.
Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks
In this work, we propose a general framework to enable joint optimization for both width configurations and weights of slimmable networks.
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness
Fine-tuning through knowledge transfer from a pre-trained model on a large-scale dataset is a widely spread approach to effectively build models on small-scale datasets.
On the Pareto Efficiency of Quantized CNN
Weight Quantization for deep convolutional neural networks (CNNs) has shown promising results in compressing and accelerating CNN-powered applications such as semantic segmentation, gesture recognition, and scene understanding.
Towards Efficient Model Compression via Learned Global Ranking
First, both the accuracy and the speed of ConvNets can affect the performance of the application.
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference
Over 46 FPGA-design experiments involving eight configurations and four data sets reveal that lightweight neural networks with a flexible $k$ value (dubbed FLightNNs) fully utilize the hardware resources on Field Programmable Gate Arrays (FPGAs), our experimental results show that FLightNNs can achieve 2$\times$ speedup when compared to lightweight NNs with $k=2$, with only 0. 1\% accuracy degradation.
Regularizing Activation Distribution for Training Binarized Deep Networks
Binarized Neural Networks (BNNs) can significantly reduce the inference latency and energy consumption in resource-constrained devices due to their pure-logical computation and fewer memory accesses.
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation.
Understanding the Impact of Label Granularity on CNN-based Image Classification
In this paper, we conduct extensive experiments using various datasets to demonstrate and analyze how and why training based on fine-grain labeling, such as "Persian cat" can improve CNN accuracy on classifying coarse-grain classes, in this case "cat."
Differentiable Training for Hardware Efficient LightNNs
To reduce runtime and resource utilization of Deep Neural Networks (DNNs) on customized hardware, LightNN has been proposed by constraining the weights of DNNs to be a sum of a limited number (denoted as $k\in\{1, 2\}$) of powers of 2.
Domain Specific Approximation for Object Detection
There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles.
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks
Resource-efficient convolution neural networks enable not only the intelligence on edge devices but also opportunities in system-level optimization such as scheduling.
Designing Adaptive Neural Networks for Energy-Constrained Image Classification
We cast the design of adaptive CNNs as a hyper-parameter optimization problem with respect to energy, accuracy, and communication constraints imposed by the mobile device.
