An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

Identification of Moisture Content in Cotton Bale by Microwave Imaging

Understanding of cotton quality is important in order to properly identify the moisture content .Measurement of moisture is difficult particularly at harvest and through the gin, because of the influence these processes have different fibre quality. Dry cotton can be harvested cleanly and efficiently but may suffer undue damage in the gin. On the other hand harvesting and ginning wet cotton leads to significant issues in processing and quality. A number of methods are used to measure moisture in seed cotton, lint and fuzzy seed, each has its varying advantages. A moisture variation of the bales that is not monitored from the outside of the bale. This research examines a new microwave imaging technique to view the internal moisture variations of cotton bale. Tests on the developed imaging sensor showed the ability to resolve small structures of parameters, against a low standard background, that were less than 1 cm in width. The accuracy of the sensing structure was also shown to provide the ability to accurately determine parameter standards. A preliminary test of the imaging capabilities on a wet commercial bale showed the technique was able to accurately image and determines the location of the wet layer within the bale

Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive which makes them unsuitable for mW-devices such as IoT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth and system-level efficiency that are crucial for deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness

Ultra-Low Power Optical Interface Circuits for Nearly Invisible Wireless Sensor Nodes.

Technological advances in the semiconductor industry and integrated circuit design have resulted in electronic devices that are smaller and cheaper than ever, and yet they are more pervasive and powerful than what could hardly be imagined several decades ago. Nowadays, small hand-held devices such as smartphones have completely reshaped the way people communicate, share information, and get entertained. According to Bell’s Law, the next generation of computers will be cubic-millimeter-scale in volume with more prevalent presence than any other computing platform available today, opening up myriad of new applications. In this dissertation, a millimeter-scale wireless sensor node for visual sensing applications is proposed, with emphasis on the optical interface circuits that enable wireless optical communication and visual imaging. Visual monitoring and imaging with CMOS image sensors opens up a variety of new applications for wireless sensor nodes, ranging from surveillance to in vivo molecular imaging. In particular, the ability to detect motion can enable intelligent power management through on-demand duty cycling and reduce the data storage requirement. Optical communication provides an ultra-low power method to wirelessly control or transmit data to the sensor node after encapsulation and deployment. The proposed wireless sensor node is a nearly-invisible, yet a complete system with imaging, optics, two-way wireless communication, CPU, memory, battery and energy harvesting with solar cells. During its ultra-low power motion detection mode, the overall power consumption is merely 304 nW, allowing energy autonomous continuous operation with 10 klux of background lighting. Such complete features in the unprecedented form factor can revolutionize the role of electronics in our future daily lives, taking the “Smart Dust” concept from fiction to reality.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110399/1/coolkgh_1.pd

Optimization of Energy Harvesting Mobile Nodes Within Scalable Converter System Based on Reinforcement Learning

Microgrid monitoring focusing on power data, such as voltage and current, has become more significant in the development of decentralized power supply system. The power data transmission delay between distributed generator is vital for evaluating the stability and financial outcome of overall grid performance. In this thesis, both hardware and simulation has been discussed for optimizing the data packets transmission delay, energy consumption, and collision rate. To minimize the transmission delay and collision rate, state-action-reward-state-action (SARSA) and Q-learning method based on Markov decision process (MDP) model is used to search the most efficient data transmission scheme for each agent device. A training process comparison between SARSA and Q-learning is given out for representing the training speed of these two methodologies in the scenario of source-relaying-destination model. To balance the exploration and exploitation process involved in these two methods, a parameter is introduced to optimize the cost time of training process. Finally, the simulation result of average throughput and data packets collision rate in the network with 20 agent nodes is presented to indicate the application feasibility of reinforcement learning algorithm in the development of scalable network. The results show that, the average throughput and collision rate stay on the expected ideal performance level for the overall network when the number of nodes is not too large. Also, the hardware development based on Bluetooth Low Energy (BLE) is used to reveal the process of data packets transmission

Low-Noise Energy-Efficient Sensor Interface Circuits

Today, the Internet of Things (IoT) refers to a concept of connecting any devices on network where environmental data around us is collected by sensors and shared across platforms. The IoT devices often have small form factors and limited battery capacity; they call for low-power, low-noise sensor interface circuits to achieve high resolution and long battery life. This dissertation focuses on CMOS sensor interface circuit techniques for a MEMS capacitive pressure sensor, thermopile array, and capacitive microphone. Ambient pressure is measured in the form of capacitance. This work propose two capacitance-to-digital converters (CDC): a dual-slope CDC employs an energy efficient charge subtraction and dual comparator scheme; an incremental zoom-in CDC largely reduces oversampling ratio by using 9b zoom-in SAR, significantly improving conversion energy. An infrared gesture recognition system-on-chip is then proposed. A hand emits infrared radiation, and it forms an image on a thermopile array. The signal is amplified by a low-noise instrumentation chopper amplifier, filtered by a low-power 30Hz LPF to remove out-band noise including the chopper frequency and its harmonics, and digitized by an ADC. Finally, a motion history image based DSP analyzes the waveform to detect specific hand gestures. Lastly, a microphone preamplifier represents one key challenge in enabling voice interfaces, which are expected to play a dominant role in future IoT devices. A newly proposed switched-bias preamplifier uses switched-MOSFET to reduce 1/f noise inherently.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137061/1/chaseoh_1.pd

Design of Low-Cost Energy Harvesting and Delivery Systems for Self-Powered Devices: Application to Authentication IC

This thesis investigates the development of low-cost energy harvesting and delivery systems for low-power low-duty-cycle devices. Initially, we begin by designing a power management scheme for on-demand power delivery. The baseline implementation is also used to identify critical challenges for low-power energy harvesting. We further propose a robust self-powered energy harvesting and delivery system (EHDS) design as a solution to achieve energy autonomy in standalone systems. The design demonstrates a complete ecosystem for low-overhead pulse-frequency modulated (PFM) harvesting while reducing harvesting window confinement and overall implementation footprint. Two transient-based models are developed for improved accuracy during design space exploration and optimization for both PFM power conversion and energy harvesting. Finally, a low-power authentication IC is demonstrated and projected designs for self-powered System-on-Chips (SoCs) are presented. The proposed designs are proto-typed in two test-chips in a 65nm CMOS process and measurement data showcase improved performance in terms of battery power, cold-start duration, passives (inductance and capacitance) needed, and end-to-end harvesting/conversion efficiency.Ph.D