Reconfigurable Architectures and Systems for IoT Applications

abstract: Internet of Things (IoT) has become a popular topic in industry over the recent years, which describes an ecosystem of internet-connected devices or things that enrich the everyday life by improving our productivity and efficiency. The primary components of the IoT ecosystem are hardware, software and services. While the software and services of IoT system focus on data collection and processing to make decisions, the underlying hardware is responsible for sensing the information, preprocess and transmit it to the servers. Since the IoT ecosystem is still in infancy, there is a great need for rapid prototyping platforms that would help accelerate the hardware design process. However, depending on the target IoT application, different sensors are required to sense the signals such as heart-rate, temperature, pressure, acceleration, etc., and there is a great need for reconfigurable platforms that can prototype different sensor interfacing circuits. This thesis primarily focuses on two important hardware aspects of an IoT system: (a) an FPAA based reconfigurable sensing front-end system and (b) an FPGA based reconfigurable processing system. To enable reconfiguration capability for any sensor type, Programmable ANalog Device Array (PANDA), a transistor-level analog reconfigurable platform is proposed. CAD tools required for implementation of front-end circuits on the platform are also developed. To demonstrate the capability of the platform on silicon, a small-scale array of 24×25 PANDA cells is fabricated in 65nm technology. Several analog circuit building blocks including amplifiers, bias circuits and filters are prototyped on the platform, which demonstrates the effectiveness of the platform for rapid prototyping IoT sensor interfaces. IoT systems typically use machine learning algorithms that run on the servers to process the data in order to make decisions. Recently, embedded processors are being used to preprocess the data at the energy-constrained sensor node or at IoT gateway, which saves considerable energy for transmission and bandwidth. Using conventional CPU based systems for implementing the machine learning algorithms is not energy-efficient. Hence an FPGA based hardware accelerator is proposed and an optimization methodology is developed to maximize throughput of any convolutional neural network (CNN) based machine learning algorithm on a resource-constrained FPGA.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

가변기능형 아날로그 블록 기반의 현장 프로그램이 가능한 혼성 신호 집적회로의 설계

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 김재하.Fast-emerging electronic device applications demand a variety of new mixed-signal ICs to be developed in fast cycle and with low cost. While field-programmable gate arrays (FPGAs) are established solutions for timely and low-cost prototyping of digital systems, their counterpart for mixed-signal circuits is still an active area for research. This thesis presents a design of a field-programmable IC for analog/mixed-signal circuits, which solves many challenges with the previous works by performing analog functions in time domain. In order to realize the field-programmable analog functionality, time-domain configurable analog block (TCAB) is proposed. A single TCAB can be programmed to various analog circuits, including a time-to-digital converter, digitally-controlled oscillator, digitally-controlled delay cell, digital pulse-width modulator, and phase interpolator. In addition, the TCABs convey and process analog information using the frequency, pulse width, delay, or phase of digital pulses or pulse sequences, rather than using analog voltage or current signals for less susceptibility to attenuation and noise. This analog information expressed in the digital pulses makes it easy to implement scalable programmable interconnects among the TCABs. The architecture of field-programmable IC capable of emulating todays diverse mixed-signal systems is also introduced. In addition to the TCABs, the proposed IC also includes arrays of configurable logic blocks (CLBs) and programmable arithmetic logic units (ALUs) for programmable digital functions. By programming the functionality of the TCAB, CLB, and ALU arrays and configuring the interconnects, the chip can implement various mixed-signal systems. A prototype IC fabricated with 65-nm CMOS technology demonstrates the versatile programmability of the proposed TCAB and the IC by being successfully operated as a 1-GHz phase-locked loop with a 12.3-psrms integrated jitter, as a 50-MS/s analog-to-digital converter with a 32.5-dB SNDR, and as a 1.2-to-0.7V DC–DC converter with 95.5 % efficiency.CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATIONS 1 1.2 THESIS CONTRIBUTION AND ORGANIZATION 5 CHAPTER 2 TIME-DOMAIN CONFIGURABLE ANALOG BLOCK 7 2.1 OVERVIEW OF THE TCAB 9 2.1.1. RECONFIGURABLE FUNCTIONALITY 9 2.1.2. TIME-DOMAIN SIGNAL PROCESSING 14 2.2 CIRCUIT IMPLEMENTATION OF THE TCAB 17 2.3 VERSATILE PROGRAMMABILITY OF TCAB 24 2.3.1. RELAXATION OSCILLATOR 24 2.3.2. DIGITALLY-CONTROLLED OSCILLATOR 28 2.3.3. DIGITAL PULSE-WIDTH MODULATOR 32 2.3.4. GATED OSCILLATOR 34 2.3.5. DIGITALLY-CONTROLLED DELAY CELL 35 2.3.6. PHASE INTERPOLATOR 37 2.3.7. MULTIPHASE DCO 39 2.3.8. NON-OVERLAPPING PULSE GENERATOR 41 2.4 TCAB ARRAY WITH PROGRAMMABLE INTERCONNECTS 43 2.4.1. TCAB ARRAY COMPOSITION 43 2.4.2. PROGRAMMABLE INTERCONNECTS 44 CHAPTER 3 PROPOSED ARCHITECTURE FOR FIELD-PROGRAMMABLE MIXED-SIGNAL IC 49 CHAPTER 4 CIRCUIT IMPLEMENTATION 54 4.1 CONFIGURABLE LOGIC BLOCK ARRAY 55 4.1.1. CONFIGURABLE LOGIC BLOCK 55 4.1.2. CLB ARRAY 56 4.2 ARITHMETIC LOGIC UNIT ARRAY 58 4.2.1. ARITHMETIC LOGIC UNIT 58 4.2.2. ALU ARRAY 61 4.3 INTERFACING BLOCKS 63 4.3.1. VOLTAGE-TO-TIME CONVERTER 64 4.3.2. PHASE-FREQUENCY DETECTOR 65 4.3.3. COUNTER BLOCK 66 4.3.4. TIME-TO-VOLTAGE CONVERTER 68 4.4 PROGRAM METHOD 70 CHAPTER 5 MIXED-SIGNAL EXAMPLES AND EXPERIMENTAL RESULTS 73 5.1 MEASUREMENT RESULTS OF TCAB 76 5.1.1. DIGITAL PULSE-WIDTH MODULATOR 76 5.1.2. DIGITALLY-CONTROLLED OSCILLATOR 79 5.1.3. GATED OSCILLATOR 81 5.2 DIGITAL PHASE-LOCKED LOOP 83 5.3 ANALOG-TO-DIGITAL CONVERTER 89 5.4 DCDC CONVERTER 94 CHAPTER 6 CONCLUSION 99 BIBLIOGRAPHY 101 초 록 108Docto