Publicaciones del IMSE

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Autor: Manuel Jiménez Través
Año: Desde 2002

Artículos de revistas


Experimental Demonstration of Coupled Differential Oscillator Networks for Versatile Applications
M. Jiménez, J. Núñez, J. Shamsi, B. Linares-Barranco and M.J. Avedillo
Journal Paper · Frontiers in Neuroscience, Neuromorphic Engineering, vol. 17, 2023
resumen      doi      

Oscillatory Neural Networks (ONNs) exhibit a high potential for energy-efficient computing. In ONNs, neurons are implemented with oscillators and synapses with resistive and/or capacitive coupling between pairs of oscillators. Computing is carried out on the basis of the rich, complex, nonlinear synchronization dynamics of a system of coupled oscillators. The exploited synchronization phenomena in ONNs are an example of fully parallel collective computing.A fast system´s convergence to stable states, which correspond to the desired processed information, enables an energy-efficient solution if small area and low-power oscillators are used, specifically, when they are built on the basis of the hysteresis exhibited by phase-transition materials such as VO2. In recent years, there have been numerous studies on ONNs using VO2. Most of them report simulation results. Although in some cases experimental results are also shown, they don´t implement the design techniques that other works on electrical simulations report that allow to improve the behavior of the ONNs.Experimental validation of these approaches is necessary. Therefore, in this work, we describe an ONN realized in a commercial CMOS technology in which the oscillators are built using a circuit that we have developed to emulate the VO2 device. The purpose is to be able to study in depth the synchronization dynamics of relaxation oscillators similar to those that can be performed with VO2 devices. The fabricated circuit is very flexible. It allows programming the synapses to implement different ONNs, calibrating the frequency of the oscillators or controlling their initialization. It uses differential oscillators and resistive synapses equivalent to the use of memristors. In this article, the designed and fabricated circuit is described in detail and experimental results are shown. Specifically, its satisfactory operation as an associative memory is demonstrated. The experiments carried out allow us to conclude that the ONN must be operated according to the type of computational task to be solved, and guidelines are extracted in this regard.

Learning Algorithms for Oscillatory Neural Networks as Associative Memory for Pattern Recognition
M. Jiménez, M.J. Avedillo, B. Linares-Barranco and J. Núñez
Journal Paper · Frontiers in Neuroscience, Neuromorphic Engineering, vol. 17, 2023
resumen      doi      

Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO2 constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich nonlinear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network, hence Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. An extensive amount of literature is available about learning in Hopfield networks, with information regarding many different learning algorithms that perform better than the Hebbian rule. However, not all of these algorithms are useful for ONN training due to the constraints imposed by their physical implementation. This paper evaluates different learning methods with respect to their suitability for ONNs. It proposes a new approach, which is compared against previous works. The proposed method has been shown to produce competitive results in terms of pattern recognition accuracy with reduced precision in synaptic weights, and to be suitable for online learning.

Operating Coupled VO2-based Oscillators for Solving Ising Models
M.J. Avedillo, M. Jiménez, C. Delacour, A. Todri-Sanial, B. Linares-Barranco and J. Núñez
Journal Paper · IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023
resumen      doi      

Coupled nano-oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. The potential of phase transition devices for such dynamical systems has recently been recognized. This paper investigates the implementation of coupled VO2-based oscillator networks to solve combinatorial optimization problems. The target problem is mapped to an Ising model, which is solved by the synchronization dynamics of the system. Different factors that impact the probability of the system reaching the ground state of the Ising Hamiltonian and, therefore, the optimum solution to the corresponding optimization problem, are analyzed. The simulation-based analysis has led to the proposal of a novel Second-Harmonic Injection Locking (SHIL) schedule. Its main feature is that SHIL signal amplitude is repeatedly smoothly increased and decreased. Reducing SHIL strength is the mechanism that enables escaping from local minimum energy states. Our experiments show better results in terms of success probability than previously reported approaches. An experimental Oscillatory Ising Machine (OIM) has been built to validate our proposal.

How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase
A. Todri-Sanial, S. Carapezzi, C. Delacour, M. Abernot, T. Gil, Elisabetta Corti, S.F. Karg, J. Nüñez, M. Jiménez, M.J. Avedillo and B. Linares-Barranco
Journal Paper · IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp 1996-2009, 2021
resumen      doi      

Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model--information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.

Digital Implementation of Oscillatory Neural Network for Image Recognition Applications
M. Abernot, T. Gil, M. Jiménez, J. Núñez, M.J. Avellido, B. Linares-Barranco, T. Gonos, T. Hardelin and A. Todri-Sanial
Journal Paper · Frontiers in Neuroscience, vol. 15, article 713054, 2021
resumen      doi      

Computing paradigm based on von Neuman architectures cannot keep up with the ever-increasing data growth (also called ‘data deluge gap ’). This has resulted in investigating novel computing paradigms and design approaches at all levels from materials to system-level implementations and applications. An alternative computing approach based on artificial neural networks uses oscillators to compute or Oscillatory Neural Networks (ONNs). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. Here, we address a fundamental problem: can we efficiently perform artificial intelligence applications with ONNs? We present a digital ONN implementation to show a proof-of-concept of the ONN approach of ‘computing-in-phase’ for pattern recognition applications. To the best of our knowledge, this is the first attempt to implement an FPGA-based fully-digital ONN. We report ONN accuracy, training, inference, memory capacity, operating frequency, hardware resources based on simulations and implementations of 5 × 3 and 10 × 6 ONNs. We present the digital ONN implementation on FPGA for pattern recognition applications such as performing digits recognition from a camera stream. We discuss practical challenges and future directions in implementing digital ONN.

Insights into the Dynamics of Coupled VO2 Oscillators for ONNs
J. Núñez, J.M. Quintana, M.J. Avedillo, M. Jiménez, A. Todri-Sanial, E. Corti, S. Karg and B. Linares-Barranco
Journal Paper · IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 10, pp 3356-3360, 2021
resumen      doi      

The collective behavior of many coupled oscillator systems is currently being explored for the implementation of different non-conventional computing paradigms. In particular, VO2 based nano-oscillators have been proposed to implement oscillatory neural networks that can serve as associative memories, useful in pattern recognition applications. Although the dynamics of a pair of coupled oscillators have already been extensively analyzed, in this paper, the topic is addressed more practically. Firstly, for the application mentioned above, each oscillator needs to be initialized in a given phase to represent the input pattern. We demonstrate the impact of this initialization mechanism on the final phase relationship of the oscillators. Secondly, such oscillatory networks are based on frequency synchronization, in which the impact of variability is critical. We carried out a comprehensive mathematical analysis of a pair of coupled oscillators taking into account both issues, which is a first step towards the design of the oscillatory neural networks for associative memory applications.

Oscillatory Neural Networks using VO2 based Phase Encoded Logic
J. Núñez, M.J. Avedillo, M. Jiménez, J.M. Quintana, A. Todri-Sanial, E. Corti, S. Karg and B. Linares-Barranco
Journal Paper · Frontiers in Neuroscience, vol. 15, article 655823, 2021
resumen      doi      pdf

Nano-oscillators based on phase-transition materials are being explored for the implementation of different non-conventional computing paradigms. In particular, vanadium dioxide (VO 2) devices are used to design autonomous non-linear oscillators from which oscillatory neural networks (ONNs) can be developed. In this work, we propose a new architecture for ONNs in which sub-harmonic injection locking (SHIL) is exploited to ensure that the phase information encoded in each neuron can only take two values. In this sense, the implementation of ONNs from neurons that inherently encode information with two-phase values has advantages in terms of robustness and tolerance to variability present in VO2 devices. Unlike conventional interconnection schemes, in which the sign of the weights is coded in the value of the resistances, in our proposal the negative (positive) weights are coded using static inverting (non-inverting) logic at the output of the oscillator. The operation of the proposed architecture is shown for pattern recognition applications.

Hybrid Phase Transition FET Devices for Logic Computation
M. Jiménez, J. Núñez and M.J. Avedillo
Journal Paper · IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 6, no. 1, pp 1-8, 2020
resumen      doi      pdf

Hybrid Phase Transition FETs (HyperFETs), built by connecting a phase transition material (PTM) to the source terminal of a FET, are able to increase the ON to OFF current ratio. In this paper, we describe a comprehensive study carried out to explore the potential of these devices for low-power and energy-limited logic applications. HyperFETs with different ONOFF current tradeoffs are evaluated at circuit level. The results show limited improvement over conventional transistors in terms of power and energy. However, on the basis of this analysis, the paper proposes different design techniques to overcome the drawbacks identified in the study and thereby make better use of HyperFETs. Hybrid circuits, using both FinFETs and HyperFETs, and circuits combining different HyperFET devices are introduced and evaluated. At some frequencies, reductions of over 40% were obtained with respect to FinFET-only implementations, while minimum energy per operation values were obtained which were lower than those achieved with low standby power (LSTP) FinFETs and high performance (HP) FinFETs. The paper also evaluates the impact of PTM transition time on the power performance of HyperFET circuits.

Phase Transition FETs for Improved Dynamic Logic Gates
M.J. Avedillo, M. Jiménez and J. Núñez
Journal Paper · IEEE Electron Device Letters, vol. 39, no. 11, pp 1776-1779, 2018
resumen      doi      pdf

Transistors incorporating phase change materials (Phase Change FETs) are being investigated to obtain steep switching and a boost in the ION/IOFF ratio and, thus, to solve power and energy limitations of CMOS technologies. In addition to the replacement of the transistors in conventional static CMOS logic circuits, the distinguishing features of Phase Change FETs can be exploited in other application domains or can be useful for solving specific design challenges. In this paper, we take advantage of them to implement a smart dynamic gate in which undesirable contention currents are reduced, leading to speed advantage without power penalties.

Congresos


Exploitation of Subharmonic Injection Locking for Solving Combinatorial Optimization Problems with Coupled Oscillators using VO2 based devices
J. Núñez, M.J. Avedillo and M. Jiménez
Conference · International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design SMACD 2023
resumen     

Abstract not available

Energy-efficient Brain-inspired Oscillatory Neural Networks using Phase-Transition Material
M. Jiménez, B. Linares-Barranco, M.J. Avedillo and J. Núñez
Conference · Workshop on Deep Learning meets Neuromorphic Hardware. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML PKDD 2023
resumen     

Oscillatory Neural Network (ONN) is a promising neuromorphic computing approach which uses networks of frequency-locked coupled oscillators, and their inherent parallel synchronization to compute. Also, ONN can be im-plemented using phase-transition materials using nano-scale area, low voltage amplitude and reduced power consumption, being an efficient way to im-plement oscillator-based computing. In state-of-theart, ONN is built with a fully-connected architecture, with coupling configured depending on the tar-get application. Its most widespread use has been as associative memory, but recently it is gathering interest as a solver for non-deterministic polynomial time problem (NP-hard). This is performed on the basis of encoding the NP-problem in the Ising model, so ONN operates as an Ising machine. ONN state naturally evolves to minimum points in the Hamiltonian energy function re-sorting to its rich non-lineal dynamics, supposing a promising paradigm of fast, low-power, parallel computation.

Experimental Demonstration of Associative Memory in Coupled Differential Oscillator Networks
M. Jiménez, J. Núñez, J. Shamsi, B. Linares-Barranco and M.J. Avedillo
Conference · XXXVIII Conference on Design of Circuits and Integrated Systems DCIS 2023
resumen     

The utilization of phase-transition materials-based nano-oscillators is being investigated to apply various non-traditional computing paradigms. Specifically, vanadium dioxide (VO2) devices are used to design self-sustained non-linear oscillators that can be employed for oscillatory neural networks (ONNs). In addition, in these ONN architectures sub-harmonic injection locking (SHIL) can be exploited to ensure that each neuron's phase information can only adopt one of two possible values. An integrated circuit demonstrator of an analog 9-neuron ONN using a deep-submicron commercial technology have been designed and fabricated. The oscillators forming the neurons closely resemble those designed using VO2 devices. The capability of the fabricated ONN to work as an associative memory has been tested. An example of two store patterns has been used to show that the ONN successfully stores the two patterns and exhibits the associative memory functionality.

Novel Iterative Hebbian Learning Rule for Oscillatory Associative Memory
M. Jiménez, M.J. Avedillo, B. Linares-Barranco and J. Núñez
Conference · XXXVIII Conference on Design of Circuits and Integrated Systems DCIS 2023
resumen     

Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich nonlinear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network. Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. Other approaches that perform better than the Hebbian rule are not useful for ONN training due to the constraints imposed by its physical implementation. This paper proposes a new approach and compares it with previous work. The proposed method has been shown to produce competitive results in terms of pattern recognition accuracy with reduced precision in synaptic weights, and to be suitable for online learning.

Hardware Oscillatory Neural Networks
M. Jiménez
Conference · XXXVII Conference on Design of Circuits and Integrated Systems DCIS 2022
resumen     

Abstract not available

Enhancing Storage Capabilities of Oscillatory Neural Networks as Associative Memory
M. Jiménez, M.J. Avedillo, J. Núñez and B. Linares-Barranco
Conference · XXXVII Conference on Design of Circuits and Integrated Systems DCIS 2022
resumen     

Abstract not available

Solving Combinatorial Optimization Problems with Coupled Phase Transition based Oscillators
J. Núñez, M.J. Avedillo and M. Jiménez
Conference · XXXVII Conference on Design of Circuits and Integrated Systems DCIS 2022
resumen     

Abstract not available

Oscillatory Neural Networks for Obstacle Avoidance on Mobile Surveillance Robot E4
M. Abernot, T. Gil, E. Kurylin, T. Hardelin, A. Magueresse,T. Gonos, M. Jiménez-Través, M.J. Avedillo de Juan and A. Todri-Sanial
Conference · IEEE International Joint Conference on Neural Networks IJCNN 2022
resumen     

Neuromorphic computing aims to emulate biological neural functions to overcome the memory bottleneck challenges with the current Von Neumann computing paradigm by enabling efficient and low-power computations. In recent years, there has been a tremendous engineering effort to bring neuromorphic computing for processing at the edge. Oscillatory Neural Networks (ONNs) are braininspired neural networks made of oscillators to mimic neuronal brain waves, typically visible on Electroencephalograms (EEG). ONNs provide massive parallelism using coupled oscillators and low power computation using oscillator phase dynamics. In this paper, we present for the first time how to use ONNs to perform obstacle avoidance on a mobile robot. Digitally implemented ONNs on FPGA are used and configured for obstacle avoidance inside the industrial surveillance robot E4 from the company, A.I.Mergence. We show that ONNs can perform real-time obstacle avoidance based on the sensory data from proximity sensors embedded on the E4 robot. The highly parallel architecture of ONNs not only allows fast real-time computation for obstacle avoidance applications but also opens up a novel computing paradigm for edge AI to enable low power and real-time sensing to action computing.

FeFETs for Phase Encoded Oscillatory based Computing
J. Núñez, M. Jiménez, B. Linares-Barranco and M.J. Avedillo
Conference · Design, Automation and Test in Europe DATE 2022
resumen     

Coupled nano-oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. The potential of Ferroelectric Field-Effect Transistor (FeFET) for such applications has recently been recognized, which is a step towards the physical realization given their ease of cointegration with commercial CMOS technologies. This paper investigates the design of oscillators using FeFETs and their potential for oscillator-based computing in which information is encoded in phase. As applications, we present the operation of FeFET coupled oscillators systems for graph coloring and Max-Cut problems, including subharmonic injection mechanism to discretize the phase in the second one.

FeFETs for Phase Encoded Oscillatory based Computing
J. Nunez, M. Jimenez and M.J. Avedillo
Conference · Conference on Design of Circuits and Integrated Systems DCIS 2021
resumen     

Coupled oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. Coupled nano-oscillator implementations using emerging devices have arisen, but the immaturity of these technologies has allowed only simple experimental demonstrations. The potential of Ferroelectric Field-Effect Transistor (FeFET) for such applications has recently been recognized, which is a step towards the physical realization given their ease of cointegration with commercial CMOS technologies. This paper investigates the design of oscillators using FeFETs and their potential for oscillatory-based computing (OBC) in which information is encoded in phase. After analyzing the FeFET-based oscillator, the operation of an Oscillatory Hopfield Neural Network (OHNN) for image classification applications in a 2x2 size example is illustrated. Finally, it is shown that this type of oscillator can also be combined with a subharmonic injection mechanism to discretize the phase as it is required in coupled oscillator’s networks for solving combinatorial optimization problems.

An Approach to the Device-Circuit Co-Design of HyperFET Circuits
M. Jiménez, J. Núñez and M.J. Avedillo
Conference · IEEE International Symposium on Circuits and Systems ISCAS 2020
resumen     

In this paper, we describe device-circuit co-design experiments for Hybrid Phase Transition FETs (HyperFETs). HyperFET transistors, built by connecting a phase transition material (PTM) to the source terminal of a FET, are able to increase the ON current without triggering the OFF current. This enables reducing supply voltage and so power consumption. HyperFETs with different ON-OFF currents tradeoffs are analyzed. Inverter chains and ring oscillators built with them are evaluated in terms of power and compared to reference designs using FETs alone. Power reductions up to 32% are shown for a HyperFET with similar OFF current and higher ON current than its FET counterpart when nodes frequently switch. However, power penalties by a factor of 400 have been obtained for other simulation stimuli. Our results identify switching activity as critical for obtaining power savings and suggest guidance both at device and circuit level to take full advantage of these devices.

Device circuit co-design of HyperFET transistors
J. Núñez, M. Jiménez and M.J. Avedillo
Conference · Conference on Design of Circuits and Integrated Systems DCIS 2019
resumen     

In this paper, we describe device-circuit co-design experiments for Hybrid Phase Transition FETs (HyperFETs). HyperFET transistors, built by connecting a phase transition material (PTM) to the source terminal of a FET, are able to increase the ON current without triggering the OFF current. This enables reducing supply voltage and so power consumption. HyperFETs with different ON-OFF currents tradeoffs are analyzed. Inverter chains and ring oscillators built with them are evaluated in terms of power and compared to reference designs using FETs alone. Power reductions up to 50% are shown for a HyperFET with similar OFF current and higher ON current than its FET counterpart when nodes frequently switch. However, power penalties by a factor of 80 have been obtained for other simulation stimuli. Our results identify switching activity as critical for obtaining power advantages from the supply voltage reduction permitted by HyperFETs, and suggest guidance both at device and circuit level to take full advantage of these devices.

Inverting Versus Non-Inverting Dynamic Logic for Two-Phase Latch-free Nanopipelines
H.J. Quintero, M. Jimenez, M.J. Avedillo and J. Núñez
Conference · Int. Conf. on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design SMACD 2018
resumen      doi      

Very fine grained latch-free pipelines are successfully used in critical parts of high performance systems. These approaches are based in Domino logic and multi-phase clock schemes. Reducing the number of logic levels per clock phase and the number of phases to the minimum is a potential way to push the limits of speed. However the implementation of such architectures with just one logic level per clock phase and two clock phases is a challenge which requires extremely full-custom design and exhibits robustness concerns. In this paper we show that the non-inverting feature of Domino plays a critical role in these difficulties. We analyze and compare the performance of two-phase gate-level pipelines implemented with Domino and with ILP, an inverting dynamic gate we have proposed. Our experiments confirm that ILP pipelines are much more robust and could simplify design.

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