Ultra Low Power Circuits for Internet of Things and Deep Learning Accelerator Design with In-Memory Computing

Collecting data from environment and converting gathered data into information is the key idea of Internet of Things (IoT). Miniaturized sensing devices enable the idea for many applications including health monitoring, industrial sensing, and so on. Sensing devices typically have small form factor and thus, low battery capacity, but at the same time, require long life time for continuous monitoring and least frequent battery replacement. This thesis introduces three analog circuit design techniques featuring ultra-low power consumption for such requirements: (1) An ultra-low power resistor-less current reference circuit, (2) A 110nW resistive frequency locked on-chip oscillator as a timing reference, (3) A resonant current-mode wireless power receiver and battery charger for implantable systems. Raw data can be efficiently transformed into useful information using deep learning. However deep learning requires tremendous amount of computation by its nature, and thus, an energy efficient deep learning hardware is highly demanded to fully utilize this algorithm in various applications. This thesis also presents a pulse-width based computation concept which utilizes in-memory computing of SRAM.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144173/1/myungjun_1.pd

Circuit Techniques for Low-Power and Secure Internet-of-Things Systems

The coming of Internet of Things (IoT) is expected to connect the physical world to the cyber world through ubiquitous sensors, actuators and computers. The nature of these applications demand long battery life and strong data security. To connect billions of things in the world, the hardware platform for IoT systems must be optimized towards low power consumption, high energy efficiency and low cost. With these constraints, the security of IoT systems become a even more difficult problem compared to that of computer systems. A new holistic system design considering both hardware and software implementations is demanded to face these new challenges. In this work, highly robust and low-cost true random number generators (TRNGs) and physically unclonable functions (PUFs) are designed and implemented as security primitives for secret key management in IoT systems. They provide three critical functions for crypto systems including runtime secret key generation, secure key storage and lightweight device authentication. To achieve robustness and simplicity, the concept of frequency collapse in multi-mode oscillator is proposed, which can effectively amplify the desired random variable in CMOS devices (i.e. process variation or noise) and provide a runtime monitor of the output quality. A TRNG with self-tuning loop to achieve robust operation across -40 to 120 degree Celsius and 0.6 to 1V variations, a TRNG that can be fully synthesized with only standard cells and commercial placement and routing tools, and a PUF with runtime filtering to achieve robust authentication, are designed based upon this concept and verified in several CMOS technology nodes. In addition, a 2-transistor sub-threshold amplifier based "weak" PUF is also presented for chip identification and key storage. This PUF achieves state-of-the-art 1.65% native unstable bit, 1.5fJ per bit energy efficiency, and 3.16% flipping bits across -40 to 120 degree Celsius range at the same time, while occupying only 553 feature size square area in 180nm CMOS. Secondly, the potential security threats of hardware Trojan is investigated and a new Trojan attack using analog behavior of digital processors is proposed as the first stealthy and controllable fabrication-time hardware attack. Hardware Trojan is an emerging concern about globalization of semiconductor supply chain, which can result in catastrophic attacks that are extremely difficult to find and protect against. Hardware Trojans proposed in previous works are based on either design-time code injection to hardware description language or fabrication-time modification of processing steps. There have been defenses developed for both types of attacks. A third type of attack that combines the benefits of logical stealthy and controllability in design-time attacks and physical "invisibility" is proposed in this work that crosses the analog and digital domains. The attack eludes activation by a diverse set of benchmarks and evades known defenses. Lastly, in addition to security-related circuits, physical sensors are also studied as fundamental building blocks of IoT systems in this work. Temperature sensing is one of the most desired functions for a wide range of IoT applications. A sub-threshold oscillator based digital temperature sensor utilizing the exponential temperature dependence of sub-threshold current is proposed and implemented. In 180nm CMOS, it achieves 0.22/0.19K inaccuracy and 73mK noise-limited resolution with only 8865 square micrometer additional area and 75nW extra power consumption to an existing IoT system.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138779/1/kaiyuan_1.pd