LOT: Logic Optimization with Testability - new transformations for logic synthesis

A new approach to optimize multilevel logic circuits is introduced. Given a multilevel circuit, the synthesis method optimizes its area while simultaneously enhancing its random pattern testability. The method is based on structural transformations at the gate level. New transformations involving EX-OR gates as well as Reed–Muller expansions have been introduced in the synthesis of multilevel circuits. This method is augmented with transformations that specifically enhance random-pattern testability while reducing the area. Testability enhancement is an integral part of our synthesis methodology. Experimental results show that the proposed methodology not only can achieve lower area than other similar tools, but that it achieves better testability compared to available testability enhancement tools such as tstfx. Specifically for ISCAS-85 benchmark circuits, it was observed that EX-OR gate-based transformations successfully contributed toward generating smaller circuits compared to other state-of-the-art logic optimization tools

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

DESIGN AND TEST OF DIGITAL CIRCUITS AND SYSTEMS USING CMOS AND EMERGING RESISTIVE DEVICES

The memristor is an emerging nano-device. Low power operation, high density, scalability, non-volatility, and compatibility with CMOS Technology have made it a promising technology for memory, Boolean implementation, computing, and logic systems. This dissertation focuses on testing and design of such applications. In particular, we investigate on testing of memristor-based memories, design of memristive implementation of Boolean functions, and reliability and design of neuromorphic computing such as neural network. In addition, we show how to modify threshold logic gates to implement more functions. Although memristor is a promising emerging technology but is prone to defects due to uncertainties in nanoscale fabrication. Fast March tests are proposed in Chapter 2 that benefit from fast write operations. The test application time is reduced significantly while simultaneously reducing the average test energy per cell. Experimental evaluation in 45 nm technology show a speed-up of approximately 70% with a decrease in energy by approximately 40%. DfT schemes are proposed to implement the new test methods. In Chapter 3, an Integer Linear Programming based framework to identify current-mode threshold logic functions is presented. It is shown that threshold logic functions can be implemented in CMOS-based current mode logic with reduced transistor count when the input weights are not restricted to be integers. Experimental results show that many more functions can be implemented with predetermined hardware overhead, and the hardware requirement of a large percentage of existing threshold functions is reduced when comparing to the traditional CMOS-based threshold logic implementation. In Chapter 4, a new method to implement threshold logic functions using memristors is presented. This method benefits from the high range of memristor’s resistivity which is used to define different weight values, and reduces significantly the transistor count. The proposed approach implements many more functions as threshold logic gates when comparing to existing implementations. Experimental results in 45 nm technology show that the proposed memristive approach implements threshold logic gates with less area and power consumption. Finally, Chapter 5 focuses on current-based designs for neural networks. CMOS aging impacts the total synaptic current and this impacts the accuracy. Chapter 5 introduces an enhanced memristive crossbar array (MCA) based analog neural network architecture to improve reliability due to the aging effect. A built-in current-based calibration circuit is introduced to restore the total synaptic current. The calibration circuit is a current sensor that receives the ideal reference current for non-aged column and restores the reduced sensed current at each column to the ideal value. Experimental results show that the proposed approach restores the currents with less than 1% precision, and the area overhead is negligible

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Energy Efficient Neocortex-Inspired Systems with On-Device Learning

Shifting the compute workloads from cloud toward edge devices can significantly improve the overall latency for inference and learning. On the contrary this paradigm shift exacerbates the resource constraints on the edge devices. Neuromorphic computing architectures, inspired by the neural processes, are natural substrates for edge devices. They offer co-located memory, in-situ training, energy efficiency, high memory density, and compute capacity in a small form factor. Owing to these features, in the recent past, there has been a rapid proliferation of hybrid CMOS/Memristor neuromorphic computing systems. However, most of these systems offer limited plasticity, target either spatial or temporal input streams, and are not demonstrated on large scale heterogeneous tasks. There is a critical knowledge gap in designing scalable neuromorphic systems that can support hybrid plasticity for spatio-temporal input streams on edge devices. This research proposes Pyragrid, a low latency and energy efficient neuromorphic computing system for processing spatio-temporal information natively on the edge. Pyragrid is a full-scale custom hybrid CMOS/Memristor architecture with analog computational modules and an underlying digital communication scheme. Pyragrid is designed for hierarchical temporal memory, a biomimetic sequence memory algorithm inspired by the neocortex. It features a novel synthetic synapses representation that enables dynamic synaptic pathways with reduced memory usage and interconnects. The dynamic growth in the synaptic pathways is emulated in the memristor device physical behavior, while the synaptic modulation is enabled through a custom training scheme optimized for area and power. Pyragrid features data reuse, in-memory computing, and event-driven sparse local computing to reduce data movement by ~44x and maximize system throughput and power efficiency by ~3x and ~161x over custom CMOS digital design. The innate sparsity in Pyragrid results in overall robustness to noise and device failure, particularly when processing visual input and predicting time series sequences. Porting the proposed system on edge devices can enhance their computational capability, response time, and battery life

Assessing the reliability of adaptive power system protection schemes

Adaptive power system protection can be used to improve the performance of existing protection schemes under certain network conditions. However, their deployment in the field is impeded by their perceived inferior reliability compared to existing protection arrangements. Moreover, their validation can be problematic due to the perceived high likelihood of the occurrence of failure modes or incorrect setting selection with variable network conditions. Reliability (including risk assessment) is one of the decisive measures that can be used in the process of verifying adaptive protection scheme performance. This paper proposes a generic methodology for assessing the reliability of adaptive protection. The method involves the identification of initiating events and scenarios that lead to protection failures and quantification of the probability of the occurrence of each failure. A numerical example of the methodology for an adaptive distance protection scheme is provided

NASA Space Engineering Research Center for VLSI systems design

This annual review reports the center's activities and findings on very large scale integration (VLSI) systems design for 1990, including project status, financial support, publications, the NASA Space Engineering Research Center (SERC) Symposium on VLSI Design, research results, and outreach programs. Processor chips completed or under development are listed. Research results summarized include a design technique to harden complementary metal oxide semiconductors (CMOS) memory circuits against single event upset (SEU); improved circuit design procedures; and advances in computer aided design (CAD), communications, computer architectures, and reliability design. Also described is a high school teacher program that exposes teachers to the fundamentals of digital logic design

A novel path delay fault simulator using binary logic

A novel path delay fault simulator for combinational logic circuits which is capable of detecting both robust and nonrobust paths is presented. Particular emphasis has been given for the use of binary logic rather than the multiple-valued logic as used in the existing simulators which contributes to the reduction of the overall complexity of the algorithm. A rule based approach has been developed which identifies all robust and nonrobust paths tested by a two-pattern test <V1,V2>, while backtracing from the POs to PIs in a depth-first manner. Rules are also given to find probable glitches and to determine how they propagate through the circuit, which enables the identification of nonrobust paths. Experimental results on several ISCAS'85 benchmark circuits demonstrate the efficiency of the algorithm