Edge analytics support industrial Internet of Things by pushing some data processing capacity to the edge of the network instead of sending the streaming data captured by the sensor nodes directly to the cloud. It is advantageous to endow machine learners for data reduction with suitable security primitives for privacy protection in edge computing devices to conserve area and power consumption. In this paper, we propose a novel physical unclonable function (PUF) based on current mirror array (CMA) circuits that reuses the circuit implementation of a machine learner-the extreme learning machine (ELM), which is a randomized neural network. Seven different challenge activation and response readout schemes are proposed to realize different weak and strong PUF functions from within the same CMA array. ELM endowed with such reconfigurable challenge-response mechanism is more robust and adaptable to different authentication protocols and security functions. Measurement results on 0.35μm test chips demonstrate that the proposed strong PUF outperforms other state-of-the-art designs with smaller area/bit of 9 × 10 ^-36 μm ² and lower native bit error rate (BER) of 0.16% with an added overhead of less than 2.5% power and 2.9% area over the native ELM implementation.

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel’s size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN’s problem: that it is difficult to fully extract the sEMG’s temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%.

Energy-efficient machine-learning and physical unclonable function (PUF) has drawn significant attention for Internet-of-Things (IoT) application in wake-up detection for bandwidth/computation reduction and privacy protection at sensor node or autonomous device. A machine-learning and PUF engine for IoT applications is presented in this paper with a current mirror cross-bar (CMCB) being a shared core circuit for both functions, leading to reduction in overhead area by 48.5×. A novel dimension expansion technique is proposed to increase weight matrix dimension beyond the physically implemented array with small hardware and energy overhead. A signed multiply-accumulation is realized in CMCB with differential current path and 2-phase conversion. The proposed engine achieves an error rate of 6.34% on MNIST digit recognition task with an energy efficiency of 2.86 TOPS/W. The PUF achieves a native bit error rate of 2.3% across corners and extremely low area per challenge response pair (CRP) of 4.17×10 ^-59 μm ² /CRP due to exponentially more CRP enabled by ternary input mode.

Simultaneous and proportional control (SPC) based on surface electromyographic (sEMG) signals has led to a broad range of applications. However, due to the limitation in the generalization and stability of current machine learning algorithms, these methods can only estimate less than 15 simultaneuous and proportional (SP) categories of finger movement. In this article, a novel deep learning algorithm, named multiattention feature fusion network (MAFN), is proposed to estimate comprehensive finger movement (up to 28 categories SP movements) from sEMG signals. MAFN is based on the multihead attention mechanism, which adaptively extracts essential features for analyzing the joint angles from the extracted sEMG features. Furthermore, a real-time exponential smoothing algorithm is designed for further improvement of the prediction stability. MAFN was evaluated on 28 finger movements of 38 subjects in the Ninapro_db2 dataset, and benchmarked with the state-of-the-art methods, such as temporal convolutional network (TCN) and long short term memory network (LSTM). The results demonstrated that the average Pearson correlation coefficient, root mean squared error of MAFN (0.84 ± 0.03,0.09 ± 0.01) were significantly higher than those of TCN (0.52 ± 0.06, p < 0.001;0.16 ± 0.02, p < 0.001) and LSTM (0.62 ± 0.06, p < 0.001;0.13 ± 0.01, p < 0.001). These improvements led to more stable and accurate movement predictions. Additionally, the time delay and power consumption of MAFN when applied to sEMG signals on a portable device are only 83.4 ms and 3 W, which implies prospective commercial applications.

Drones have been widely used in everyday life and they can help deal with various tasks, including photography, searching, and surveillance. Nonetheless, it is difficult for drones to perform customized online real-time object detection. In this study, we propose an intelligent real-time object detection system for drones. It is composed of an FPGA and a drone. A neural-network (NN) engine is designed on the FPGA for NN model acceleration. The FPGA receives activation data from an NN model, which are assembled into the data stream. Multiple fetch and jump pointers catch required activation values from the data stream, which are then filtered and sent to each thread independently. To accelerate processing speed, multiple processing elements (PEs) deal with tasks in parallel by using multiple weights and threads. The image data are transferred from the drone host to the FPGA, which are tackled with high speed by the NN engine. The NN engine results are returned to the host, which is used to adjust the flying route accordingly. Experimental results reveal that our proposed FPGA design well utilizes FPGA computing resources with 81.56% DSP and 72.80% LUT utilization rates, respectively. By using the Yolov3-tiny model for fast object detection, our system can detect objects at the speed of 8 frames per second and achieves a much lower power consumption compared to state-of-the-art methods. More importantly, the intelligent object detection techniques provide more pixels for the target of interest and they can increase the detection confidence score from 0.74 to 0.90 and from 0.70 to 0.84 for persons and cars, respectively.