Ultra-Low Power Circuit Design for Cubic-Millimeter Wireless Sensor Platform.

Modern daily life is surrounded by smaller and smaller computing devices. As Bell’s Law predicts, the research community is now looking at tiny computing platforms and mm3-scale sensor systems are drawing an increasing amount of attention since they can create a whole new computing environment. Designing mm3-scale sensor nodes raises various circuit and system level challenges and we have addressed and proposed novel solutions for many of these challenges to create the first complete 1.0mm3 sensor system including a commercial microprocessor. We demonstrate a 1.0mm3 form factor sensor whose modular die-stacked structure allows maximum volume utilization. Low power I2C communication enables inter-layer serial communication without losing compatibility to standard I2C communication protocol. A dual microprocessor enables concurrent computation for the sensor node control and measurement data processing. A multi-modal power management unit allowed energy harvesting from various harvesting sources. An optical communication scheme is provided for initial programming, synchronization and re-programming after recovery from battery discharge. Standby power reduction techniques are investigated and a super cut-off power gating scheme with an ultra-low power charge pump reduces the standby power of logic circuits by 2-19× and memory by 30%. Different approaches for designing low-power memory for mm3-scale sensor nodes are also presented in this work. A dual threshold voltage gain cell eDRAM design achieves the lowest eDRAM retention power and a 7T SRAM design based on hetero-junction tunneling transistors reduces the standby power of SRAM by 9-19× with only 15% area overhead. We have paid special attention to the timer for the mm3-scale sensor systems and propose a multi-stage gate-leakage-based timer to limit the standard deviation of the error in hourly measurement to 196ms and a temperature compensation scheme reduces temperature dependency to 31ppm/°C. These techniques for designing ultra-low power circuits for a mm3-scale sensor enable implementation of a 1.0mm3 sensor node, which can be used as a skeleton for future micro-sensor systems in variety of applications. These microsystems imply the continuation of the Bell’s Law, which also predicts the massive deployment of mm3-scale computing systems and emergence of even smaller and more powerful computing systems in the near future.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91438/1/sori_1.pd

Design and analysis of SRAMs for energy harvesting systems

PhD ThesisAt present, the battery is employed as a power source for wide varieties of microelectronic systems ranging from biomedical implants and sensor net-works to portable devices. However, the battery has several limitations and incurs many challenges for the majority of these systems. For instance, the design considerations of implantable devices concern about the battery from two aspects, the toxic materials it contains and its lifetime since replacing the battery means a surgical operation. Another challenge appears in wire-less sensor networks, where hundreds or thousands of nodes are scattered around the monitored environment and the battery of each node should be maintained and replaced regularly, nonetheless, the batteries in these nodes do not all run out at the same time. Since the introduction of portable systems, the area of low power designs has witnessed extensive research, driven by the industrial needs, towards the aim of extending the lives of batteries. Coincidentally, the continuing innovations in the field of micro-generators made their outputs in the same range of several portable applications. This overlap creates a clear oppor-tunity to develop new generations of electronic systems that can be powered, or at least augmented, by energy harvesters. Such self-powered systems benefit applications where maintaining and replacing batteries are impossi-ble, inconvenient, costly, or hazardous, in addition to decreasing the adverse effects the battery has on the environment. The main goal of this research study is to investigate energy harvesting aware design techniques for computational logic in order to enable the capa- II bility of working under non-deterministic energy sources. As a case study, the research concentrates on a vital part of all computational loads, SRAM, which occupies more than 90% of the chip area according to the ITRS re-ports. Essentially, this research conducted experiments to find out the design met-ric of an SRAM that is the most vulnerable to unpredictable energy sources, which has been confirmed to be the timing. Accordingly, the study proposed a truly self-timed SRAM that is realized based on complete handshaking protocols in the 6T bit-cell regulated by a fully Speed Independent (SI) tim-ing circuitry. The study proved the functionality of the proposed design in real silicon. Finally, the project enhanced other performance metrics of the self-timed SRAM concentrating on the bit-line length and the minimum operational voltage by employing several additional design techniques.Umm Al-Qura University, the Ministry of Higher Education in the Kingdom of Saudi Arabia, and the Saudi Cultural Burea

Robust Circuit Design for Low-Voltage VLSI.

Voltage scaling is an effective way to reduce the overall power consumption, but the major challenges in low voltage operations include performance degradation and reliability issues due to PVT variations. This dissertation discusses three key circuit components that are critical in low-voltage VLSI. Level converters must be a reliable interface between two voltage domains, but the reduced on/off-current ratio makes it extremely difficult to achieve robust conversions at low voltages. Two static designs are proposed: LC2 adopts a novel pulsed-operation and modulates its pull-up strength depending on its state. A 3-sigma robustness is guaranteed using a current margin plot; SLC inherently reduces the contention by diode-insertion. Improvements in performance, power, and robustness are measured from 130nm CMOS test chips. SRAM is a major bottleneck in voltage-scaling due to its inherent ratioed-bitcell design. The proposed 7T SRAM alleviates the area overhead incurred by 8T bitcells and provides robust operation down to 0.32V in 180nm CMOS test chips with 3.35fW/bit leakage. Auto-Shut-Off provides a 6.8x READ energy reduction, and its innate Quasi-Static READ has been demonstrated which shows a much improved READ error rate. A use of PMOS Pass-Gate improves the half-select robustness by directly modulating the device strength through bitline voltage. Clocked sequential elements, flip-flops in short, are ubiquitous in today’s digital systems. The proposed S2CFF is static, single-phase, contention-free, and has the same number of devices as in TGFF. It shows a 40% power reduction as well as robust low-voltage operations in fabricated 45nm SOI test chips. Its simple hold-time path and the 3.4x improvement in 3-sigma hold-time is presented. A new on-chip flip-flop testing harness is also proposed, and measured hold-time variations of flip-flops are presented.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111525/1/yejoong_1.pd

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

Power Management and SRAM for Energy-Autonomous and Low-Power Systems

We demonstrate the two first-known, complete, self-powered millimeter-scale computer systems. These microsystems achieve zero-net-energy operation using solar energy harvesting and ultra-low-power circuits. A medical implant for monitoring intraocular pressure (IOP) is presented as part of a treatment for glaucoma. The 1.5mm3 IOP monitor is easily implantable because of its small size and measures IOP with 0.5mmHg accuracy. It wirelessly transmits data to an external wand while consuming 4.7nJ/bit. This provides rapid feedback about treatment efficacies to decrease physician response time and potentially prevent unnecessary vision loss. A nearly-perpetual temperature sensor is presented that processes data using a 2.1μW near-threshold ARM°R Cortex- M3TM μP that provides a widely-used and trusted programming platform. Energy harvesting and power management techniques for these two microsystems enable energy-autonomous operation. The IOP monitor harvests 80nW of solar power while consuming only 5.3nW, extending lifetime indefinitely. This allows the device to provide medical information for extended periods of time, giving doctors time to converge upon the best glaucoma treatment. The temperature sensor uses on-demand power delivery to improve low-load dc-dc voltage conversion efficiency by 4.75x. It also performs linear regulation to deliver power with low noise, improved load regulation, and tight line regulation. Low-power high-throughput SRAM techniques help millimeter-scale microsystems meet stringent power budgets. VDD scaling in memory decreases energy per access, but also decreases stability margins. These margins can be improved using sizing, VTH selection, and assist circuits, as well as new bitcell designs. Adaptive Crosshairs modulation of SRAM power supplies fixes 70% of parametric failures. Half-differential SRAM design improves stability, reducing VMIN by 72mV. The circuit techniques for energy autonomy presented in this dissertation enable millimeter-scale microsystems for medical implants, such as blood pressure and glucose sensors, as well as non-medical applications, such as supply chain and infrastructure monitoring. These pervasive sensors represent the continuation of Bell’s Law, which accurately traces the evolution of computers as they become smaller, more numerous, and more powerful. The development of millimeter-scale massively-deployed ubiquitous computers ensures the continued expansion and profitability of the semiconductor industry. NanoWatt circuit techniques will allow us to meet this next frontier in IC design.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86387/1/grgkchen_1.pd

Design and Analysis of Robust Low Voltage Static Random Access Memories.

Static Random Access Memory (SRAM) is an indispensable part of most modern VLSI designs and dominates silicon area in many applications. In scaled technologies, maintaining high SRAM yield becomes more challenging since they are particularly vulnerable to process variations due to 1) the minimum sized devices used in SRAM bitcells and 2) the large array sizes. At the same time, low power design is a key focus throughout the semiconductor industry. Since low voltage operation is one of the most effective ways to reduce power consumption due to its quadratic relationship to energy savings, lowering the minimum operating voltage (Vmin) of SRAM has gained significant interest. This thesis presents four different approaches to design and analyze robust low voltage SRAM: SRAM analysis method improvement, SRAM bitcell development, SRAM peripheral optimization, and advance device selection. We first describe a novel yield estimation method for bit-interleaved voltage-scaled 8-T SRAMs. Instead of the traditional trade-off between write and read, the trade-off between write and half select disturb is analyzed. In addition, this analysis proposes a method to find an appropriate Write Word-Line (WWL) pulse width to maximize yield. Second, low leakage 10-T SRAM with speed compensation scheme is proposed. During sleep mode of a sensor application, SRAM retaining data cannot be shut down so it is important to minimize leakage in SRAM. This work adopts several leakage reduction techniques while compensating performance. Third, adaptive write architecture for low voltage 8-T SRAMs is proposed. By adaptively modulating WWL width and voltage level, it is possible to achieve low power consumption while maintaining high yield without excessive performance degradation. Finally, low power circuit design based on heterojunction tunneling transistors (HETTs) is discussed. HETTs have a steep subthreshold swing beneficial for low voltage operation. Device modeling and design of logic and SRAM are proposed.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91569/1/daeyeonk_1.pd

Ultra Low Power FPGA-Based Architecture for Wake-up Radio in Wireless Sensor Networks

In this paper the capabilities of ultra low power FPGAs to implement Wake-up Radios (WuR) for ultra low energy Wireless Sensor Networks (WSNs) are analyzed. The main goal is to evaluate the utilization of very low power configurable devices to take advantage of their speed, flexibility and low power consumption instead of the more common approaches based on ASICs or microcontrollers. In this context, energy efficiency is a key aspect, considering that usually the instant power consumption is considered a figure of merit, more than the total energy consumed by the application

FORCED STACK SLEEP TRANSISTOR (FORTRAN): A NEW LEAKAGE CURRENT REDUCTION APPROACH IN CMOS BASED CIRCUIT DESIGNING

Reduction in leakage current has become a significant concern in nanotechnology-based low-power, low-voltage, and high-performance VLSI applications. This research article discusses a new low-power circuit design the approach of FORTRAN (FORced stack sleep TRANsistor), which decreases the leakage power efficiency in the CMOS-based circuit outline in VLSI domain. FORTRAN approach reduces leakage current in both active as well as standby modes of operation. Furthermore, it is not time intensive when the circuit goes from active mode to standby mode and vice-versa. To validate the proposed design approach, experiments are conducted in the Tanner EDA tool of mentor graphics bundle on projected circuit designs for the full adder, a chain of 4-inverters, and 4-bit multiplier designs utilizing 180nm, 130nm, and 90nm TSMC technology node. The outcomes obtained show the result of a 95-98% vital reduction in leakage power as well as a 15-20% reduction in dynamic power with a minor increase in delay. The result outcomes are compared for accuracy with the notable design approaches that are accessible for both active and standby modes of operation

Energy-Efficient Wireless Circuits and Systems for Internet of Things

As the demand of ultra-low power (ULP) systems for internet of thing (IoT) applications has been increasing, large efforts on evolving a new computing class is actively ongoing. The evolution of the new computing class, however, faced challenges due to hard constraints on the RF systems. Significant efforts on reducing power of power-hungry wireless radios have been done. The ULP radios, however, are mostly not standard compliant which poses a challenge to wide spread adoption. Being compliant with the WiFi network protocol can maximize an ULP radio’s potential of utilization, however, this standard demands excessive power consumption of over 10mW, that is hardly compatible with in ULP systems even with heavy duty-cycling. Also, lots of efforts to minimize off-chip components in ULP IoT device have been done, however, still not enough for practical usage without a clean external reference, therefore, this limits scaling on cost and form-factor of the new computer class of IoT applications. This research is motivated by those challenges on the RF systems, and each work focuses on radio designs for IoT applications in various aspects. First, the research covers several endeavors for relieving energy constraints on RF systems by utilizing existing network protocols that eventually meets both low-active power, and widespread adoption. This includes novel approaches on 802.11 communication with articulate iterations on low-power RF systems. The research presents three prototypes as power-efficient WiFi wake-up receivers, which bridges the gap between industry standard radios and ULP IoT radios. The proposed WiFi wake-up receivers operate with low power consumption and remain compatible with the WiFi protocol by using back-channel communication. Back-channel communication embeds a signal into a WiFi compliant transmission changing the firmware in the access point, or more specifically just the data in the payload of the WiFi packet. With a specific sequence of data in the packet, the transmitter can output a signal that mimics a modulation that is more conducive for ULP receivers, such as OOK and FSK. In this work, low power mixer-first receivers, and the first fully integrated ultra-low voltage receiver are presented, that are compatible with WiFi through back-channel communication. Another main contribution of this work is in relieving the integration challenge of IoT devices by removing the need for external, or off-chip crystals and antennas. This enables a small form-factor on the order of mm3-scale, useful for medical research and ubiquitous sensing applications. A crystal-less small form factor fully integrated 60GHz transceiver with on-chip 12-channel frequency reference, and good peak gain dual-mode on-chip antenna is presented.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162975/1/jaeim_1.pd

Containing Analog Data Deluge at Edge through Frequency-Domain Compression in Collaborative Compute-in-Memory Networks

Edge computing is a promising solution for handling high-dimensional, multispectral analog data from sensors and IoT devices for applications such as autonomous drones. However, edge devices' limited storage and computing resources make it challenging to perform complex predictive modeling at the edge. Compute-in-memory (CiM) has emerged as a principal paradigm to minimize energy for deep learning-based inference at the edge. Nevertheless, integrating storage and processing complicates memory cells and/or memory peripherals, essentially trading off area efficiency for energy efficiency. This paper proposes a novel solution to improve area efficiency in deep learning inference tasks. The proposed method employs two key strategies. Firstly, a Frequency domain learning approach uses binarized Walsh-Hadamard Transforms, reducing the necessary parameters for DNN (by 87% in MobileNetV2) and enabling compute-in-SRAM, which better utilizes parallelism during inference. Secondly, a memory-immersed collaborative digitization method is described among CiM arrays to reduce the area overheads of conventional ADCs. This facilitates more CiM arrays in limited footprint designs, leading to better parallelism and reduced external memory accesses. Different networking configurations are explored, where Flash, SA, and their hybrid digitization steps can be implemented using the memory-immersed scheme. The results are demonstrated using a 65 nm CMOS test chip, exhibiting significant area and energy savings compared to a 40 nm-node 5-bit SAR ADC and 5-bit Flash ADC. By processing analog data more efficiently, it is possible to selectively retain valuable data from sensors and alleviate the challenges posed by the analog data deluge.Comment: arXiv admin note: text overlap with arXiv:2307.03863, arXiv:2309.0177