Recent publications
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
FRONTIERS ISSN: 1662-453X
abstract
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
FRONTIERS ISSN: 1662-453X
abstract
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.
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
abstract
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
abstract
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
abstract
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
abstract
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.
Exploring Open-Source and Proprietary Design Tools to Implement a Symmetric Cipher on FPGAs
P. Navarro-Torrero, L.F. Rojas-Muñoz, P. Brox and S. Sánchez-Solano
Conference · XXXVIII Conference on Design of Circuits and Integrated Systems DCIS 2023
abstract
Abstract not available
A Simple Power Analysis of an FPGA implementation of a polynomial multiplier for the NTRU cryptosystem
E. Camacho-Ruiz, S. Sánchez-Solano, M.C. Martínez-Rodríguez, E. Tena-Sánchez and P. Brox
Conference · XXXVIII Conference on Design of Circuits and Integrated Systems DCIS 2023
abstract
Abstract not available
HW/SW implementation of RSA digital signature on a RISC-V-based System-on-Chip
A. Karmakar, S. Sánchez-Solano, M.C. Martínez-Rodríguez and P. Brox
Conference · XXXVIII Conference on Design of Circuits and Integrated Systems DCIS 2023
abstract
Abstract not available
A complete SHA-3 hardware library based on a high efficiency Keccak design
E. Camacho-Ruiz, S. Sánchez-Solano, M.C. Martínez-Rodríguez and P. Brox
Conference · IEEE Nordic Circuits and Systems Conference (NorCAS), 2023
abstract
Hash functions are a crucial part of the cryptographic primitives. So much so that in 2007 a new competition was launched to select new standards for the SHA-3 function, which was won by Keccak. Since then, many software and hardware implementations have been submitted, claiming to reduce the number of operation cycles or increase design efficiency. Thus, this work aims to present a new hardware solution for the Keccak function, which forms the core of SHA-3, that achieves a high degree of tunability and is competitive with the state of the art. In addition, this work presents the integration of these designs into a hardware IP module together with the relevant drivers and functions that allow their use in software environments. Preliminary tests have shown an acceleration of up to 10 times compared to pure software code.
