An Adiabatic Capacitive Artificial Neuron With RRAM-Based Threshold Detection for Energy-Efficient Neuromorphic Computing

In the quest for low power, bio-inspired computation both memristive and memcapacitive-based Artificial Neural Networks (ANN) have been the subjects of increasing focus for hardware implementation of neuromorphic computing. One step further, regenerative capacitive neural networks, which call for the use of adiabatic computing, offer a tantalising route towards even lower energy consumption, especially when combined with `memimpedace' elements. Here, we present an artificial neuron featuring adiabatic synapse capacitors to produce membrane potentials for the somas of neurons; the latter implemented via dynamic latched comparators augmented with Resistive Random-Access Memory (RRAM) devices. Our initial 4-bit adiabatic capacitive neuron proof-of-concept example shows 90% synaptic energy saving. At 4 synapses/soma we already witness an overall 35% energy reduction. Furthermore, the impact of process and temperature on the 4-bit adiabatic synapse shows a maximum energy variation of 30% at 100 degree Celsius across the corners without any functionality loss. Finally, the efficacy of our adiabatic approach to ANN is tested for 512 & 1024 synapse/neuron for worst and best case synapse loading conditions and variable equalising capacitance's quantifying the expected trade-off between equalisation capacitance and range of optimal power-clock frequencies vs. loading (i.e. the percentage of active synapses).Comment: This work has been accepted to the IEEE TCAS-

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

Surpassing Fundamental Limits through Time Varying Electromagnetics

Surpassing the fundamental limits that govern all electromagnetic structures, such as reciprocity and the delay-bandwidth-size limit, will have a transformative impact on all applications based on electromagnetic circuits and systems. For instance, violating principles of reciprocity enables non-reciprocal components such as isolators and circulators, which find application in full-duplex wireless radios, radar, biomedical imaging, and quantum computing systems. Overcoming the delay-bandwidth-size limit enables ultra-broadband yet extremely-compact devices whose size is not fundamentally related to the wavelength at the operating frequency. The focus of this dissertation is on using time-variance as a new toolbox to overcome these fundamental limits and re-imagine circuit and system design. Traditional non-reciprocal components are realized using ferrite materials that loose their reciprocity under the application of external magnetic bias. However, the sheer volume, cost and weight of these magnet based non-reciprocal components coupled with their inability to be fabricated in conventional semiconductor processes, have limited their application to bulky and large-scale systems. Other approaches such as active-biased and non-linearity based non-reciprocity are compatible with semiconductor processes, however, they suffer from other poor linearity and noise performance. In this dissertation, using passive transistor switch as the modulating element, we have proposed the concept of spatio-temporal conductivity modulation and have demonstrated a gamut of non-reciprocal devices ranging from gyrators to isolators and circulators. Through novel circuit topologies, for the first time, we have demonstrated on-chip circulators with multi-watt input power handling, operation at high millimeter-wave frequencies, and tailor made circulators for emerging technologies such as simultaneous-transmit-and-receive MRI and quantum computing. Delay-bandwidth-size trade-off is another fundamental electromagnetic limit, that constrains the delay imparted by a medium or a device within a fixed footprint to be inversely proportional to the signal bandwidth. It is this limit that governs the size of any microwave passive devices to be inversely proportional to its operating frequency. As a part of this dissertation, through intelligent clocking of switched capacitor networks we overcame the delay-bandwidth-size limit, thus resulting in infinitesimal, yet broadband microwave devices. Here we proposed a new paradigm in wave propagation where the properties such as the propagation delay and characteristic impedance does not depend on the constituent elements/materials of the medium, but rather heavily rely on the user-defined modulation scheme, thereby opening huge opportunities for realizing highly-reconfigurable passives. Leveraging these concepts, we demonstrated wide range of reciprocal an non-reciprocal devices including ultra-compact delay elements, highly-reconfigurable microwave passives, ultra-wideband circulators with infinitesimal form-factors and dispersion-free chip scale floquet topological insulators. Application of these devices have also been evaluated in real-world systems through our demonstrations of wideband, full-duplex receivers leveraging switched capacitors based true-time-delay interference cancelers and floquet topological insulator based antenna interfaces for full-duplex phased-arrays and ultra-wideband beamformers. Furthermore, to cater the growing RF and microwave needs of future, large-scale quantum computing systems, we demonstrated a low-cryogenic, wideband circulator based on time modulation of superconducting devices. This superconducting circulator is expected to operate alongside the superconducting qubits, inside a dilution refrigerator at 10mK-100mK, thus enabling a tightly integrated quantum system. We also presented the design and implementation of a cryogenic-CMOS clock driver chip that will generate the clocks required by the superconducting circulator. Finally, we also demonstrated the design and implementation of a low-noise, low power consumption, 6GHz - 8GHz cryogenic downconversion receiver at 4K for cryogenic qubit readout

Miniaturized Transistors

What is the future of CMOS? Sustaining increased transistor densities along the path of Moore's Law has become increasingly challenging with limited power budgets, interconnect bandwidths, and fabrication capabilities. In the last decade alone, transistors have undergone significant design makeovers; from planar transistors of ten years ago, technological advancements have accelerated to today's FinFETs, which hardly resemble their bulky ancestors. FinFETs could potentially take us to the 5-nm node, but what comes after it? From gate-all-around devices to single electron transistors and two-dimensional semiconductors, a torrent of research is being carried out in order to design the next transistor generation, engineer the optimal materials, improve the fabrication technology, and properly model future devices. We invite insight from investigators and scientists in the field to showcase their work in this Special Issue with research papers, short communications, and review articles that focus on trends in micro- and nanotechnology from fundamental research to applications

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

Electron Beam Lithography patterning of superconducting and magnetic nanostructures for novel optical and spintronic devices

In this thesis novel, high-end superconducting and spintronic devices have been fabricated and characterized. In summary, the proposed work has been focused on the realization of nanowires, and more generally nanostructures, using the Electron Beam Lithography. Such a technology offers a powerful solution for nanofabrication able to conjugate spatial resolution, operation flexibility, and costs. Two main research fields has been explored: superconductive nanowires for advanced optical detection and nanostructures for magneto-resistance based devices