27 research outputs found
Novel ultra-energy-efficient reversible designs of sequential logic quantum-dot cellular automata flip-flop circuits
The version of record of this article, first published in [The Journal of Supercomputing], is available online at Publisher’s website: http://dx.doi.org/10.1007/s11227-023-05134-1Quantum-dot cellular automata (QCA) is a technological approach to implement digital circuits with exceptionally high integration density, high switching frequency, and low energy dissipation. QCA circuits are a potential solution to the energy dissipation issues created by shrinking microprocessors with ultra-high integration densities. Current QCA circuit designs are irreversible, yet reversible circuits are known to increase energy efficiency. Thus, the development of reversible QCA circuits will further reduce energy dissipation. This paper presents novel reversible and irreversible sequential QCA set/reset (SR), data (D), Jack Kilby (JK), and toggle (T) flip-flop designs based on the majority gate that utilizes the universal, standard, and efficient (USE) clocking scheme, which allows the implementation of feedback paths and easy routing for sequential QCA-based circuits. The simulation results confirm that the proposed reversible QCA USE sequential flip-flop circuits exhibit energy dissipation less than the Landauer energy limit. Irreversible QCA USE flip-flop designs, although having higher energy dissipation, sometimes have floorplan areas and delay times less than those of reversible designs; therefore, they are also explored. The trade-offs between the energy dissipation versus the area cost and delay time for the reversible and irreversible QCA circuits are examined comprehensively
Low power predictable memory and processing architectures
Great demand in power optimized devices shows promising economic potential and draws lots of attention in industry and research area. Due to the continuously shrinking CMOS process, not only dynamic power but also static power has emerged as a big concern in power reduction. Other than power optimization, average-case power estimation is quite significant for power budget allocation but also challenging in terms of time and effort. In this thesis, we will introduce a methodology to support modular quantitative analysis in order to estimate average power of circuits, on the basis of two concepts named Random Bag Preserving and Linear Compositionality. It can shorten simulation time and sustain high accuracy, resulting in increasing the feasibility of power estimation of big systems. For power saving, firstly, we take advantages of the low power characteristic of adiabatic logic and asynchronous logic to achieve ultra-low dynamic and static power. We will propose two memory cells, which could run in adiabatic and non-adiabatic mode. About 90% dynamic power can be saved in adiabatic mode when compared to other up-to-date designs. About 90% leakage power is saved. Secondly, a novel logic, named Asynchronous Charge Sharing Logic (ACSL), will be introduced. The realization of completion detection is simplified considerably. Not just the power reduction improvement, ACSL brings another promising feature in average power estimation called data-independency where this characteristic would make power estimation effortless and be meaningful for modular quantitative average case analysis. Finally, a new asynchronous Arithmetic Logic Unit (ALU) with a ripple carry adder implemented using the logically reversible/bidirectional characteristic exhibiting ultra-low power dissipation with sub-threshold region operating point will be presented. The proposed adder is able to operate multi-functionally
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first
Performance analysis of fault-tolerant nanoelectronic memories
Performance growth in microelectronics, as described by Moore’s law, is steadily
approaching its limits. Nanoscale technologies are increasingly being explored as a
practical solution to sustaining and possibly surpassing current performance trends of
microelectronics. This work presents an in-depth analysis of the impact on performance,
of incorporating reliability schemes into the architecture of a crossbar molecular switch
nanomemory and demultiplexer. Nanoelectronics are currently in their early stages, and
so fabrication and design methodologies are still in the process of being studied and
developed. The building blocks of nanotechnology are fabricated using bottom-up
processes, which leave them highly susceptible to defects. Hence, it is very important that
defect and fault-tolerant schemes be incorporated into the design of nanotechnology
related devices.
In this dissertation, we focus on the study of a novel and promising class of
computer chip memories called crossbar molecular switch memories and their
demultiplexer addressing units. A major part of this work was the design of a defect and
fault tolerance scheme we called the Multi-Switch Junction (MSJ) scheme. The MSJ scheme takes advantage of the regular array geometry of the crossbar nanomemory to
create multiple switches in the fabric of the crossbar nanomemory for the storage of a
single bit.
Implementing defect and fault tolerant schemes come at a performance cost to the
crossbar nanomemory; the challenge becomes achieving a balance between device
reliability and performance. We have studied the reliability induced performance penalties
as they relate to the time (delay) it takes to access a bit, and the amount of power
dissipated by the process. Also, MSJ was compared to the banking and error correction
coding fault tolerant schemes. Studies were also conducted to ascertain the potential
benefits of integrating our MSJ scheme with the banking scheme. Trade-off analysis
between access time delay, power dissipation and reliability is outlined and presented in
this work.
Results show the MSJ scheme increases the reliability of the crossbar
nanomemory and demultiplexer. Simulation results also indicated that MSJ works very
well for smaller nanomemory array sizes, with reliabilities of 100% for molecular switch
failure rates in the 10% or less range
Normally-Off Computing Design Methodology Using Spintronics: From Devices to Architectures
Energy-harvesting-powered computing offers intriguing and vast opportunities to dramatically transform the landscape of Internet of Things (IoT) devices and wireless sensor networks by utilizing ambient sources of light, thermal, kinetic, and electromagnetic energy to achieve battery-free computing. In order to operate within the restricted energy capacity and intermittency profile of battery-free operation, it is proposed to innovate Elastic Intermittent Computation (EIC) as a new duty-cycle-variable computing approach leveraging the non-volatility inherent in post-CMOS switching devices. The foundations of EIC will be advanced from the ground up by extending Spin Hall Effect Magnetic Tunnel Junction (SHE-MTJ) device models to realize SHE-MTJ-based Majority Gate (MG) and Polymorphic Gate (PG) logic approaches and libraries, that leverage intrinsic-non-volatility to realize middleware-coherent, intermittent computation without checkpointing, micro-tasking, or software bloat and energy overheads vital to IoT. Device-level EIC research concentrates on encapsulating SHE-MTJ behavior with a compact model to leverage the non-volatility of the device for intrinsic provision of intermittent computation and lifetime energy reduction. Based on this model, the circuit-level EIC contributions will entail the design, simulation, and analysis of PG-based spintronic logic which is adaptable at the gate-level to support variable duty cycle execution that is robust to brief and extended supply outages or unscheduled dropouts, and development of spin-based research synthesis and optimization routines compatible with existing commercial toolchains. These tools will be employed to design a hybrid post-CMOS processing unit utilizing pipelining and power-gating through state-holding properties within the datapath itself, thus eliminating checkpointing and data transfer operations
Sensitivity Optimization for NV-Diamond Magnetometry
Solid-state spin systems including nitrogen-vacancy (NV) centers in diamond
constitute an increasingly favored quantum sensing platform. However, present
NV ensemble devices exhibit sensitivities orders of magnitude away from
theoretical limits. The sensitivity shortfall both handicaps existing
implementations and curtails the envisioned application space. This review
analyzes present and proposed approaches to enhance the sensitivity of
broadband ensemble-NV-diamond magnetometers. Improvements to the spin dephasing
time, the readout fidelity, and the host diamond material properties are
identified as the most promising avenues and are investigated extensively. Our
analysis of sensitivity optimization establishes a foundation to stimulate
development of new techniques for enhancing solid-state sensor performance.Comment: 73 pages, 36 figures, 17 table
Heterogeneous Reconfigurable Fabrics for In-circuit Training and Evaluation of Neuromorphic Architectures
A heterogeneous device technology reconfigurable logic fabric is proposed which leverages the cooperating advantages of distinct magnetic random access memory (MRAM)-based look-up tables (LUTs) to realize sequential logic circuits, along with conventional SRAM-based LUTs to realize combinational logic paths. The resulting Hybrid Spin/Charge FPGA (HSC-FPGA) using magnetic tunnel junction (MTJ) devices within this topology demonstrates commensurate reductions in area and power consumption over fabrics having LUTs constructed with either individual technology alone. Herein, a hierarchical top-down design approach is used to develop the HSCFPGA starting from the configurable logic block (CLB) and slice structures down to LUT circuits and the corresponding device fabrication paradigms. This facilitates a novel architectural approach to reduce leakage energy, minimize communication occurrence and energy cost by eliminating unnecessary data transfer, and support auto-tuning for resilience. Furthermore, HSC-FPGA enables new advantages of technology co-design which trades off alternative mappings between emerging devices and transistors at runtime by allowing dynamic remapping to adaptively leverage the intrinsic computing features of each device technology. HSC-FPGA offers a platform for fine-grained Logic-In-Memory architectures and runtime adaptive hardware. An orthogonal dimension of fabric heterogeneity is also non-determinism enabled by either low-voltage CMOS or probabilistic emerging devices. It can be realized using probabilistic devices within a reconfigurable network to blend deterministic and probabilistic computational models. Herein, consider the probabilistic spin logic p-bit device as a fabric element comprising a crossbar-structured weighted array. The Programmability of the resistive network interconnecting p-bit devices can be achieved by modifying the resistive states of the array\u27s weighted connections. Thus, the programmable weighted array forms a CLB-scale macro co-processing element with bitstream programmability. This allows field programmability for a wide range of classification problems and recognition tasks to allow fluid mappings of probabilistic and deterministic computing approaches. In particular, a Deep Belief Network (DBN) is implemented in the field using recurrent layers of co-processing elements to form an n x m1 x m2 x ::: x mi weighted array as a configurable hardware circuit with an n-input layer followed by i ≥ 1 hidden layers. As neuromorphic architectures using post-CMOS devices increase in capability and network size, the utility and benefits of reconfigurable fabrics of neuromorphic modules can be anticipated to continue to accelerate
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma