Quantum Dot Cellular Automata Check Node Implementation for LDPC Decoders

The quantum dot Cellular Automata (QCA) is an emerging nanotechnology that has gained significant research interest in recent years. Extremely small feature sizes, ultralow power consumption, and high clock frequency make QCA a potentially attractive solution for implementing computing architectures at the nanoscale. To be considered as a suitable CMOS substitute, the QCA technology must be able to implement complex real-time applications with affordable complexity. Low density parity check (LDPC) decoding is one of such applications. The core of LDPC decoding lies in the check node (CN) processing element which executes actual decoding algorithm and contributes toward overall performance and complexity of the LDPC decoder. This study presents a novel QCA architecture for partial parallel, layered LDPC check node. The CN executes Normalized Min Sum decoding algorithm and is flexible to support CN degree dc up to 20. The CN is constructed using a VHDL behavioral model of QCA elementary circuits which provides a hierarchical bottom up approach to evaluate the logical behavior, area, and power dissipation of the whole design. Performance evaluations are reported for the two main implementations of QCA i.e. molecular and magneti

On the descriptional complexity of iterative arrays

The descriptional complexity of iterative arrays (lAs) is studied. Iterative arrays are a parallel computational model with a sequential processing of the input. It is shown that lAs when compared to deterministic finite automata or pushdown automata may provide savings in size which are not bounded by any recursive function, so-called non-recursive trade-offs. Additional non-recursive trade-offs are proven to exist between lAs working in linear time and lAs working in real time. Furthermore, the descriptional complexity of lAs is compared with cellular automata (CAs) and non-recursive trade-offs are proven between two restricted classes. Finally, it is shown that many decidability questions for lAs are undecidable and not semidecidable

CA-BIST for asynchronous circuits: a case study on the RAPPID asynchronous instruction length decoder

Journal ArticleThis paper presents a case study in low-cost noninvasive Built-In Self Test (BIST) for RAPPID, a largescale 120,000-transistor asynchronous version of the Pentium® Pro Instruction Length Decoder, which runs at 3.6 GHz. RAPPID uses a synchronous 0.25 micron CMOS library for static and domino logic, and has no Design-for-Test hooks other than some debug features. We explore the use of Cellular Automata (CA) for on-chip test pattern generation and response evaluation. More specifically, we look for fast ways to tune the CA-BIST to the RAPPID design, rather than using pseudo-random testing. The metric for tuning the CA-BIST pattern generation is based on an abstract hardware description model of the instruction length decoder, which is independent of implementation details, and hence also independent of the asynchronous circuit style. Our CA-BI ST solution uses a novel bootstrap procedure for generating the test patterns, which give complete coverage for this metric, and cover 94% of the testable stuck-at faults for the actual design at switch level. Analysis of the undetected and untestable faults shows that the same fault effects can be expected for a similar clocked circuit. This is encouraging evidence that testability is no excuse to avoid asynchronous design techniques in addition to high-performance synchronous solutions

CA-BIST for asynchronous circuits: a case study on the RAPPID asynchronous instruction length decoder

Journal ArticleThis paper presents a case study in low-cost noninvasive Built-In Self Test (BIST) for RAPPID, a largescale 120,000-transistor asynchronous version of the Pentium® Pro Instruction Length Decoder, which runs at 3.6 GHz. RAPPID uses a synchronous 0.25 micron CMOS library for static and domino logic, and has no Design-for-Test hooks other than some debug features. We explore the use of Cellular Automata (CA) for on-chip test pattern generation and response evaluation. More specifically, we look for fast ways to tune the CA-BIST to the RAPPID design, rather than using pseudo-random testing. The metric for tuning the CA-BIST pattern generation is based on an abstract hardware description model of the instruction length decoder, which is independent of implementation details, and hence also independent of the asynchronous circuit style. Our CA-BI ST solution uses a novel bootstrap procedure for generating the test patterns, which give complete coverage for this metric, and cover 94% of the testable stuck-at faults for the actual design at switch level. Analysis of the undetected and untestable faults shows that the same fault effects can be expected for a similar clocked circuit. This is encouraging evidence that testability is no excuse to avoid asynchronous design techniques in addition to high-performance synchronous solutions

Deep Learning with Photonic Neural Cellular Automata

Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware. Photonics offers a promising solution by leveraging the unique properties of light. However, conventional neural network architectures, which typically require dense programmable connections, pose several practical challenges for photonic realizations. To overcome these limitations, we propose and experimentally demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning with sparse connectivity. PNCA harnesses the speed and interconnectivity of photonics, as well as the self-organizing nature of cellular automata through local interactions to achieve robust, reliable, and efficient processing. We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification. We demonstrate binary classification of images in the fashion-MNIST dataset using as few as 3 programmable photonic parameters, achieving an experimental accuracy of 98.0% with the ability to also recognize out-of-distribution data. The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of light-based computing whilst mitigating their practical challenges. Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers

Fsimac: a fault simulator for asynchronous sequential circuits

Journal ArticleAt very high frequencies, the major potential of asynchronous circuits is absence of clock skew and, through that, better exploitation of relative timing relations. This paper presents Fsimac, a gate-level fault simulator for stuck-at and gate-delay faults in asynchronous sequential circuits. Fsimac not only evaluates combinational logic and typical asynchronous gates such as Muller C-elements, but also complex domino gates, which are widely used in high-speed designs. Our algorithm for detecting feedback loops is designed so as to minimize the iterations for simulating the unfolded circuit. We use min-max timing analysis to compute the bounds on the signal delays. Stuck-at faults are detected by comparing logic values at the primary outputs against the corresponding values in the fault-free design. For delay faults, we additionally compare min-max time stamps for primary output signals. Fault coverage reported by Fsimac for pseudo-random tests generated by Cellular Automata show some very good results, but also indicate test holes for which more specific patterns are needed. We intend to deploy Fsimac for designing more effective CA-BIST