More Like this

1. E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy

   Jiang, Z; Wang, Y; Chen, X; Xu, P; Zhao, Y; Lin, Y; Wang, Z (January 2019, NeurIPS)

   Convolutional neural networks (CNNs) have been increasingly deployed to Internet of Things (IoT) devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training? We strive to reduce the energy cost during training, by dropping unnecessary computations, from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. Specifically, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring an accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving comes at the small accuracy costs of 2% (top-1) and 0.1% (top-5). 

   more » « less
2. LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference

   Yu, Zhongzhi; Fu, Yonggan; Wu, Shang; Li, Mengquan; You, Haoran; Lin, Yingyan (March 2022, tinyML Research Symposium'22)

   Low precision deep neural network (DNN) training is one of the most effective techniques for boosting DNNs’ training efficiency, as it trims down the training cost from the finest bit level. While existing works mostly fix the model precision during the whole training process, a few pioneering works have shown that dynamic precision schedules help NNs converge to a better accuracy while leading to a lower training cost than their static precision training counterparts. However, existing dynamic low precision training methods rely on manually designed precision schedules to achieve advantageous efficiency and accuracy trade-offs, limiting their more comprehensive practical applications and achievable performance. To this end, we propose LDP, a Learnable Dynamic Precision DNN training framework that can automatically learn a temporally and spatially dynamic precision schedule during training towards optimal accuracy and efficiency trade-offs. It is worth noting that LDP-trained DNNs are by nature efficient during inference. Further more, we visualize the resulting temporal and spatial precision schedule and distribution of LDP trained DNNs on different tasks to better understand the corresponding DNNs’ characteristics at different training stages and DNN layers both during and after training, drawing insights for promoting further innovations. Extensive experiments and ablation studies (seven networks, five datasets, and three tasks) show that the proposed LDP consistently outperforms state-of-the-art (SOTA) low precision DNN training techniques in terms of training efficiency and achieved accuracy trade-offs. For example, in addition to having the advantage of being automated, our LDP achieves a 0.31% higher accuracy with a 39.1% lower computational cost when training ResNet-20 on CIFAR-10 as compared with the best SOTA method. 

   more » « less
3. Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks

   https://doi.org/10.1109/TC.2020.2972520  

   Kundu, Souvik; Nazemi, Mahdi; Pedram, Massoud; Chugg, Keith M.; Beeral, Peter (February 2020, IEEE Transactions on Computers)

   The high energy cost of processing deep convolutional neural networks impedes their ubiquitous deployment in energy-constrained platforms such as embedded systems and IoT devices. This article introduces convolutional layers with pre-defined sparse 2D kernels that have support sets that repeat periodically within and across filters. Due to the efficient storage of our periodic sparse kernels, the parameter savings can translate into considerable improvements in energy efficiency due to reduced DRAM accesses, thus promising significant improvements in the trade-off between energy consumption and accuracy for both training and inference. To evaluate this approach, we performed experiments with two widely accepted datasets, CIFAR-10 and Tiny ImageNet in sparse variants of the ResNet18 and VGG16 architectures. Compared to baseline models, our proposed sparse variants require up to ∼82% fewer model parameters with 5.6× fewer FLOPs with negligible loss in accuracy for ResNet18 on CIFAR-10. For VGG16 trained on Tiny ImageNet, our approach requires 5.8× fewer FLOPs and up to ∼83.3% fewer model parameters with a drop in top-5 (top-1) accuracy of only 1.2% ( ∼2.1% ). We also compared the performance of our proposed architectures with that of ShuffleNet and MobileNetV2. Using similar hyperparameters and FLOPs, our ResNet18 variants yield an average accuracy improvement of ∼2.8% . 

   more » « less
4. An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions

   https://doi.org/10.1109/ASP-DAC58780.2024.10473884  

   Tung, Caleb; Eliopoulos, Nicholas; Jajal, Purvish; Ramshankar, Gowri; Yang, Cheng-Yun; Synovic, Nicholas; Zhang, Xuecen; Chaudhary, Vipin; Thiruvathukal, George K; Lu, Yung-Hsiang (January 2024, Asia and South Pacific Design Automation Conference (ASP-DAC))

   Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energyefficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy 

   more » « less
5. pSConv: A Pre-defined Sparse Kernel Based Convolution for Deep CNNs

   https://doi.org/10.1109/ALLERTON.2019.8919683  

   Kundu, Souvik; Prakash, Saurav; Akrami, Haleh; Beerel, Peter A.; Chugg, Keith M. (September 2019, 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton))

   The high demand for computational and storage resources severely impedes the deployment of deep convolutional neural networks (CNNs) in limited resource devices. Recent CNN architectures have proposed reduced complexity versions (e.g,. SuffleNet and MobileNet) but at the cost of modest decreases in accuracy. This paper proposes pSConv, a pre-defined sparse 2D kernel based convolution, which promises significant improvements in the trade-off between complexity and accuracy for both CNN training and inference. To explore the potential of this approach, we have experimented with two widely accepted datasets, CIFAR-10 and Tiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures. Our approach shows a parameter count reduction of up to 4.24× with modest degradation in classification accuracy relative to that of standard CNNs. Our approach outperforms a popular variant of ShuffleNet using a variant of ResNet18 with pSConv having 3 × 3 kernels with only four of nine elements not fixed at zero. In particular, the parameter count is reduced by 1.7× for CIFAR-10 and 2.29× for Tiny ImageNet with an increased accuracy of ~ 4%. 

   more » « less