Design of Low-Voltage Digital Building Blocks and ADCs for Energy-Efficient Systems

Increasing number of energy-limited applications continue to drive the demand for designing systems with high energy efficiency. This tutorial covers the main building blocks of a system implementation including digital logic, embedded memories, and analog-to-digital converters and describes the challenges and solutions to designing these blocks for low-voltage operation

Design Techniques for High Speed Low Voltage and Low Power Non-Calibrated Pipeline Analog to Digital Converters

The profound digitization of modern microelectronic modules made Analog-to- Digital converters (ADC) key components in many systems. With resolutions up to 14bits and sampling rates in the 100s of MHz, the pipeline ADC is a prime candidate for a wide range of applications such as instrumentation, communications and consumer electronics. However, while past work focused on enhancing the performance of the pipeline ADC from an architectural standpoint, little has been done to individually address its fundamental building blocks. This work aims to achieve the latter by proposing design techniques to improve the performance of these blocks with minimal power consumption in low voltage environments, such that collectively high performance is achieved in the pipeline ADC. Towards this goal, a Recycling Folded Cascode (RFC) amplifier is proposed as an enhancement to the general performance of the conventional folded cascode. Tested in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18?m Complementary Metal Oxide Semiconductor (CMOS) technology, the RFC provides twice the bandwidth, 8-10dB additional gain, more than twice the slew rate and improved noise performance over the conventional folded cascode-all at no additional power or silicon area. The direct auto-zeroing offset cancellation scheme is optimized for low voltage environments using a dual level common mode feedback (CMFB) circuit, and amplifier differential offsets up to 50mV are effectively cancelled. Together with the RFC, the dual level CMFB was used to implement a sample and hold amplifier driving a singleended load of 1.4pF and using only 2.6mA; at 200MS/s better than 9bit linearity is achieved. Finally a power conscious technique is proposed to reduce the kickback noise of dynamic comparators without resorting to the use of pre-amplifiers. When all techniques are collectively used to implement a 1Vpp 10bit 160MS/s pipeline ADC in Semiconductor Manufacturing International Corporation (SMIC) 0.18[mu]m CMOS, 9.2 effective number of bits (ENOB) is achieved with a near Nyquist-rate full scale signal. The ADC uses an area of 1.1mm2 and consumes 42mW in its analog core. Compared to recent state-of-the-art implementations in the 100-200MS/s range, the presented pipeline ADC uses the least power per conversion rated at 0.45pJ/conversion-step

High linearity analog and mixed-signal integrated circuit design

Linearity is one of the most important specifications in electrical circuits.;In Chapter 1, a ladder-based transconductance networks has been adopted first time to build a low distortion analog filters for low frequency applications. This new technique eliminated the limitation of the application with the traditional passive resistors for low frequency applications. Based on the understanding of this relationship, a strategy for designing high linear analog continuous-time filters has been developed. According to our strategy, a prototype analog integrated filter has been designed and fabricated with AMI05 0.5 um standard CMOS process. Experimental results proved this technique has the ability to provide excellent linearity with very limited active area.;In Chapter 2, the relationships between the transconductance networks and major circuit specifications have been explored. The analysis reveals the trade off between the silicon area saved by the transconductance networks and the some other important specifications such as linearity, noise level and the process variations of the overall circuit. Experimental results of discrete component circuit matched very well with our analytical outcomes to predict the change of linearity and noise performance associated with different transconductance networks.;The Chapter 3 contains the analysis and mathematical proves of the optimum passive area allocations for several most popular analog active filters. Because the total area is now manageable by the technique introduced in the Chapter 1, the further reduce of the total area will be very important and useful for efficient utilizing the silicon area, especially with the today\u27s fast growing area efficiency of the highly density digital circuits. This study presents the mathematical conclusion that the minimum passive area will be achieved with the equalized resistor and capacitor.;In the Chapter 4, a well recognized and highly honored current division circuit has been studied. Although it was claimed to be inherently linear and there are over 60 published works reported with high linearity based on this technique, our study discovered that this current division circuit can achieve, if proper circuit condition being managed, very limited linearity and all the experimental verified performance actually based on more general circuit principle. Besides its limitation, however, we invented a novel current division digital to analog converter (DAC) based on this technique. Benefiting from the simple circuit structure and moderate good linearity, a prototype 8-bit DAC was designed in TSMC018 0.2 um CMOS process and the post layout simulations exhibited the good linearity with very low power consumption and extreme small active area.;As the part of study of the output stage for the current division DAC discussed in the Chapter 4, a current mirror is expected to amplify the output current to drive the low resistive load. The strategy of achieving the optimum bandwidth of the cascode current mirror with fixed total current gain is discussed in the Chapter 5.;Improving the linearity of pipeline ADC has been the hottest and hardest topic in solid-state circuit community for decade. In the Chapter 6, a comprehensive study focus on the existing calibration algorithms for pipeline ADCs is presented. The benefits and limitations of different calibration algorithms have been discussed. Based on the understanding of those reported works, a new model-based calibration is delivered. The simulation results demonstrate that the model-based algorithms are vulnerable to the model accuracy and this weakness is very hard to be removed. From there, we predict the future developments of calibration algorithms that can break the linearity limitations for pipelined ADC. (Abstract shortened by UMI.

Challenges and Opportunities in Near-Threshold DNN Accelerators around Timing Errors

AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges and opportunities for operation in the NTC paradigm. By performing rigorous simulations in TPU systolic array, we reveal the severity of timing errors and its impact on inference accuracy at NTC. We analyze various attributes—such as data–delay relationship, delay disparity within arithmetic units, utilization pattern, hardware homogeneity, workload characteristics—and uncover unique localized and global techniques to deal with the timing errors in NTC

Architecture and Circuit Design Optimization for Compute-In-Memory

The objective of the proposed research is to optimize computing-in-memory (CIM) design for accelerating Deep Neural Network (DNN) algorithms. As compute peripheries such as analog-to-digital converter (ADC) introduce significant overhead in CIM inference design, the research first focuses on the circuit optimization for inference acceleration and proposes a resistive random access memory (RRAM) based ADC-free in-memory compute scheme. We comprehensively explore the trade-offs involving different types of ADCs and investigate a new ADC design especially suited for the CIM, which performs the analog shift-add for multiple weight significance bits, improving the throughput and energy efficiency under similar area constraints. Furthermore, we prototype an ADC-free CIM inference chip design with a fully-analog data processing manner between sub-arrays, which can significantly improve the hardware performance over the conventional CIM designs and achieve near-software classification accuracy on ImageNet and CIFAR-10/-100 dataset. Secondly, the research focuses on hardware support for CIM on-chip training. To maximize hardware reuse of CIM weight stationary dataflow, we propose the CIM training architectures with the transpose weight mapping strategy. The cell design and periphery circuitry are modified to efficiently support bi-directional compute. A novel solution of signed number multiplication is also proposed to handle the negative input in backpropagation. Finally, we propose an SRAM-based CIM training architecture and comprehensively explore the system-level hardware performance for DNN on-chip training based on silicon measurement results.Ph.D