In the natural world, energy and information are deeply entwined, mutually constraining and complementing each other. To exploit this natural merit, this paper proposes a FEI strategy: Fusion processing of sensing Energy and Information for infrared smart vision system. The proposed Information-Power-Coupler (IPCp) takes the ability of simultaneous energy harvesting and low power inpixel computing, which utilizes in-situ coupled energy to process the containing information on the same focal plane. Furthermore, a self-adaptive Intelligent-Power- Controller (IPCtrl) capable of scheduling the harvested energy to complete low power neural network inference is introduced. The implementation of IPC2 system utilizes a software-hardware co-design strategy to exploit the layer-wise characteristic of the computation process and circuit topology, achieving energy-efficient self-sustainable fusion processing of sensing energy and information. Simulation results show that the IPCtrl could supply 594.68nW with the power conversion efficiency of 93.38%, when the harvested energy from the IPCp is 636.84nW. The performance validates the self-sustainability of the system with the self-powered image recognition of a complete network running at 4fps with an accuracy of 99.4%.

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed.

Haptic interaction is critical in physical human–robot Interaction (pHRI), given its wide applications in manufacturing, medical and healthcare, and various industry tasks. A stable haptic interface is always needed while the human operator interacts with the robot. Passivity-based approaches have been widely utilized in the control design as a sufficient condition for stability. However, it is a conservative approach which therefore sacrifices performance to maintain stability. This article proposes a novel concept to characterize an ultimately passive system, which can achieve the boundedness of the energy in the steady-state. A so-called ultimately passive controller (UPC) is then proposed. This algorithm switches the system between a nominal mode for keeping desired performance and a conservative mode when needed to remain stable. An experimental evaluation on two robotic systems, one admittance-based and one impedance-based, demonstrates the potential interest of the proposed framework compared to existing approaches. The results demonstrate the possibility of UPC in finding a more aggressive tradeoff between haptic performance and system stability, while still providing a stability guarantee.

This paper proposes an ultra-low-power CMOS Image Sensor (CIS) chip based on sensing-with-computing (Senputing) architecture to reduce the power bottleneck of vision system. This Senputing chip achieves BNN 1st-layer convolution in analog domain with ultra-low power consumption. It has two working modes, Normal-Sensor (NS) mode and Direct- Photocurrent-Computation (DPC) mode. The prototype measurement results under 65nm CMOS process on MNIST classification task shows that the power of feature map computation is 2.17μW with 120fps frame rates and 98.1% accuracy. The computation efficiency reaches to 11.49TOPs/W, which is 14.8× higher than state-of-art works.

This paper proposes an in-situ self-powered BNN-based intelligent visual perception system that harvests light energy utilizing the indispensable image sensor itself. The harvested energy is allocated to the low-power BNN computation modules layer by layer, adopting a light-weighted duty-cycling-based energy scheduler. A software-hardware co-design method, which exploits the layer-wise error tolerance of BNN as well as the computing-error and energy consumption characteristics of the computation circuit, is proposed to determine the parameters of the energy scheduler, achieving high energy efficiency for self-powered BNN inference. Simulation results show that with the proposed inference-adaptive energy scheduling method, self-powered MNIST classification task can be performed at a frame rate of 4 fps if the harvesting power is 1μW, while guaranteeing at least 90% inference accuracy using binary LeNet-5 network.

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.

Educational games can facilitate teaching and learning. However, research on feedback types in educational games requires further investigation. Feedback in educational games could enhance students' learning, but it is not the more complex the feedback, the better. This study investigated the effects of different types of feedback in games on students' learning. The results indicated that when learning goals point to knowledge retention, receiving knowledge of correct response (KCR) or receiving elaborated feedback (EF) was better than receiving knowledge of results (KR). When learning goals point to knowledge transfer, receiving EF was better than receiving KCR or receiving KR, and receiving KCR was better than receiving KR. The results of this study integrate knowledge retention, knowledge transfer and learner cognitive load to inform the selection of educational game feedback types.

The scale-invariant feature transform (SIFT) algorithm is still one of the most reliable image feature extraction methods. Despite its excellent robustness on various image transformations, SIFT's intensive computational burden has been severely preventing it from being used in real-time and energy-efficient embedded machine vision systems. To reduce processing time and energy cost while executing SIFT, an analog signal processing architecture, analog signal processing (ASP)SIFT, is proposed in this article. In ASP-SIFT, the Gaussian pyramid construction, difference-of-Gaussian (DoG) pyramid construction and keypoint locating, which are the primary steps of the keypoint detection part of the SIFT algorithm, are done directly with analog circuit networks. Thus, by completing keypoint detection in the analog domain, the total processing time is approximately equal to the settling time of the circuit network. Besides, by adopting a current-mode circuit network operating in the subthreshold region, the power dissipation would be very low. Simulation results show that the total processing speed for a typical video graphics array (VGA)-format (640 × 480) image is up to 2.3 kframes per second, which is at least 3.26× faster than the state-of-the-art digital hardware accelerators, while the system power is 94.5 mW and the energy consumption is only 40 μJ per frame.

Approximate computing has drawn considerable attention to both academia and industry in the area of DNN hardware. Despite substantial efforts to design approximate circuits and building blocks, the resilience of DNN layers and structures remains an untapped field to explore. This paper presents an efficient framework to evaluate DNN resilience with fine-grained approximate operations, such as multipliers, adders and low-bit operators. The framework can execute large-scale approximate DNNs with relatively less time overhead. Massive experiments are conducted with the proposed framework to reveal the relationship between network structures and error tolerance. Additionally, a case study of fine-tuning the approximate DNN is presented.

Approximate computing is a promising paradigm to deal with large computing workloads in fault-tolerant applications, providing opportunities to improve hardware efficiency of Deep Neural Networks (DNNs). However, it is still difficult to apply highly approximate arithmetics (e.g., multipliers) to DNNs due to the effect of error accumulation and the convergence problem in re-training phase. To tackle this limitation, we propose a hardware-software co-design algorithm, namely Incremental Network Approximation (INA). By addressing the convergence problem, INA promotes fault tolerance of DNNs, and yields more tradeoffs between accuracy and implementation cost. Experiments show that the approximate inference models re-trained by INA could achieve up to 80% hardware reduction in various hardware design level, while the classification accuracy degradation is less than 2%. Moreover, the experiments also exhibit the generality of INA algorithm for applying to various approximate multiplier design.

Process Variation (PV) may cause accuracy loss of the analog neural network (ANN) processors, and make it hard to be scaled down, as well as feasibility degrading. This paper first analyses the impact of PV on the performance of ANN chips. Then proposes an in-situ transfer learning method at system level to reduce PV's influence with low-precision back-propagation. Simulation results show the proposed method could increase 50% tolerance of operating point drift and 70% ∼ 100% tolerance of mismatch with less than 1% accuracy loss of benchmarks. It also reduces 66.7% memories and has about 50× energy-efficiency improvement of multiplication in the learning stage, compared with the conventional full-precision (32bit float) training system.

A prototype of fully flexible intelligent contact lens, which are shown in the impressive action movie series of “MISSION: IMPOSSIBLE”, has become the Possible Mission in this work. Hereon, the system adopts analog-to-information processing method to build a specific Multi-Layer Perceptron network for image classification tasks with flexible devices and circuits, where the information is extracted from raw data of the sensing analog signal directly. Simulated with HSPICE of Level-62 TFT device model, for standard test image data set of MNIST, the classification accuracy of the presented flexible neural network circuit is up to 92.99%; meanwhile, the classification speed is as fast as 10k fps, and the energy consumption is low to only 15.16μJ. Additionally, for the imperfections of flexible devices of larger devices mismatch and process variations, the fault-tolerance of the system has been evaluated as well, which demonstrates the feasibility of the presented methods and lowers the barrier to integrated all kinds of FLEXIBLE Devices into a FULLY FLEXIBLE Systems with sensors, processing parts and even energy harvesting parts, etc., in the future wearable smart terminals.