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Found 14 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.
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
We also show that the proposed lightweight processing elements (LightPEs) consistently achieve Pareto-optimal results in terms of accuracy and hardware-efficiency.
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators
As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that incorporates quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models.
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
In this work, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints.
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
In this paper, we propose Virtual Pooling (ViP), a model-level approach to improve speed and energy consumption of CNN-based image classification and object detection tasks, with a provable error bound.
Single-Path NAS: Device-Aware Efficient ConvNet Design
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the latency constraint of a mobile device?
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.
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device?
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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.
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks
For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs.
