- Award ID(s):
- 1652633
- PAR ID:
- 10319133
- Date Published:
- Journal Name:
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Wearable internet of things (IoT) devices are becoming popular due to their small form factor and low cost. Potential applications include human health and activity monitoring by embedding sensors such as accelerometer, gyroscope, and heart rate sensor. However, these devices have severely limited battery capacity, which requires frequent recharging. Harvesting ambient energy and optimal energy allocation can make wearable IoT devices practical by eliminating the charging requirement. This paper presents a near-optimal runtime energy management technique by considering the harvested energy. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. We show that the results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline.more » « less
-
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
-
Quantization-Based Optimization Algorithm for Hardware Implementation of Convolution Neural Networks
Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm for CNN hardware design and model the performance metrics for the purpose of CNN implementation in resource-constrained devices (RCDs) and optimizing layers in deep neural networks (DNNs). The algorithm novelty is based on blending two quantization techniques to perform full model quantization with optimum accuracy, and without additional neurons. The algorithm is applied to a selected CNN model and implemented on an FPGA. Implementing CNN using broad data is not possible due to capacity issues. With the proposed quantization algorithm, we succeeded in implementing the model on the FPGA using 16-, 12-, and 8-bit quantization. Compared to the 16-bit design, the 8-bit design offers a 44% decrease in resource utilization, and achieves power and energy reductions of 41% and 42%, respectively. Models show that trading off one quantization bit yields savings of approximately 5.4K LUTs, 4% logic utilization, 46.9 mW power, and 147 μJ energy. The models were also used to estimate performance metrics for a sample DNN design.
-
IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.more » « less
-
The Internet of Things (IoT), forming the foundation of Cyber Physical Systems (CPS), connects a huge number of ubiquitous sensing and mobile computing devices. The mobile IoT systems generate an enormous volume of a variety of dynamic context data and typically count on centralized architectures to process them. However, their inability to ensure security and decline in communication efficiency and response time with the increase in the size of IoT network are some of the many concerning weaknesses that are holding back the fast-paced growth of IoT. Realizing the limitations of centralized systems, recently blockchain-based decentralized architecture is being considered as the key to redesigning the IoT systems in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient. However, before realizing the new promise of blockchain for IoT, there are significant challenges to address. One fundamental challenge is the scale issue around data collection, storage, and analytic as IoT sensor devices possess limited computational power and storage capabilities. In particular, since the chain is always growing, IoT devices require more and more resources. Thus, an oversized chain poses storage and scalability problems. With this in mind, the overall goal of our research is to design a lightweight scalable blockchain framework for IoT of mobile devices. This framework, coined as "Sensor-Chain", promises a new generation of lightweight blockchain management with a superior reduction in resource consumption, and at the same time capable of retaining critical information about the IoT systems of mobile devices.more » « less