Gradient Descent in Materio
- URL: http://arxiv.org/abs/2105.11233v1
- Date: Sat, 15 May 2021 12:18:31 GMT
- Title: Gradient Descent in Materio
- Authors: Marcus N. Boon, Hans-Christian Ruiz Euler, Tao Chen, Bram van de Ven,
Unai Alegre Ibarra, Peter A. Bobbert, Wilfred G. van der Wiel
- Abstract summary: We show an efficient and accurate homodyne gradient extraction method for performing gradient descent on the loss function directly in the material system.
This shows that gradient descent can in principle be fully implemented in materio using simple electronics.
- Score: 3.756477173839499
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Deep learning, a multi-layered neural network approach inspired by the brain,
has revolutionized machine learning. One of its key enablers has been
backpropagation, an algorithm that computes the gradient of a loss function
with respect to the weights in the neural network model, in combination with
its use in gradient descent. However, the implementation of deep learning in
digital computers is intrinsically wasteful, with energy consumption becoming
prohibitively high for many applications. This has stimulated the development
of specialized hardware, ranging from neuromorphic CMOS integrated circuits and
integrated photonic tensor cores to unconventional, material-based computing
systems. The learning process in these material systems, taking place, e.g., by
artificial evolution or surrogate neural network modelling, is still a
complicated and time-consuming process. Here, we demonstrate an efficient and
accurate homodyne gradient extraction method for performing gradient descent on
the loss function directly in the material system. We demonstrate the method in
our recently developed dopant network processing units, where we readily
realize all Boolean gates. This shows that gradient descent can in principle be
fully implemented in materio using simple electronics, opening up the way to
autonomously learning material systems.
Related papers
- Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks.
In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
arXiv Detail & Related papers (2024-09-17T04:48:45Z) - Neural Incremental Data Assimilation [8.817223931520381]
We introduce a deep learning approach where the physical system is modeled as a sequence of coarse-to-fine Gaussian prior distributions parametrized by a neural network.
This allows us to define an assimilation operator, which is trained in an end-to-end fashion to minimize the reconstruction error.
We illustrate our approach on chaotic dynamical physical systems with sparse observations, and compare it to traditional variational data assimilation methods.
arXiv Detail & Related papers (2024-06-21T11:42:55Z) - Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences.
It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations.
Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
arXiv Detail & Related papers (2024-02-20T15:23:24Z) - Gradient-free online learning of subgrid-scale dynamics with neural emulators [5.283819482083864]
We propose a generic algorithm to train machine learning-based subgrid parametrizations online.
We are able to train a parametrization that recovers most of the benefits of online strategies without having to compute the gradient of the original solver.
arXiv Detail & Related papers (2023-10-30T09:46:35Z) - Event-based Backpropagation for Analog Neuromorphic Hardware [0.0]
We present our progress implementing the EventProp algorithm using the example of the BrainScaleS-2 analog neuromorphic hardware.
We present the theoretical framework for estimating gradients and results verifying the correctness of the estimation.
It suggests the feasibility of a full on-device implementation of the algorithm that would enable scalable, energy-efficient, event-based learning in large-scale analog neuromorphic hardware.
arXiv Detail & Related papers (2023-02-13T18:55:59Z) - Agnostic Physics-Driven Deep Learning [82.89993762912795]
This work establishes that a physical system can perform statistical gradient learning without gradient computations.
In Aeqprop, the specifics of the system do not have to be known: the procedure is based on external manipulations.
Aeqprop also establishes that in natural (bio)physical systems, genuine gradient-based statistical learning may result from generic, relatively simple mechanisms.
arXiv Detail & Related papers (2022-05-30T12:02:53Z) - Inducing Gaussian Process Networks [80.40892394020797]
We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points.
The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains.
We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
arXiv Detail & Related papers (2022-04-21T05:27:09Z) - Physical Deep Learning with Biologically Plausible Training Method [2.5608506499175094]
We present physical deep learning by extending a biologically plausible training algorithm called direct feedback alignment.
We can emulate and accelerate the computation for this training on a simple and scalable physical system.
Our results provide practical solutions for the training and acceleration of neuromorphic computation.
arXiv Detail & Related papers (2022-04-01T05:46:16Z) - Physical Gradients for Deep Learning [101.36788327318669]
We find that state-of-the-art training techniques are not well-suited to many problems that involve physical processes.
We propose a novel hybrid training approach that combines higher-order optimization methods with machine learning techniques.
arXiv Detail & Related papers (2021-09-30T12:14:31Z) - Efficient Differentiable Simulation of Articulated Bodies [89.64118042429287]
We present a method for efficient differentiable simulation of articulated bodies.
This enables integration of articulated body dynamics into deep learning frameworks.
We show that reinforcement learning with articulated systems can be accelerated using gradients provided by our method.
arXiv Detail & Related papers (2021-09-16T04:48:13Z) - Accurately Solving Physical Systems with Graph Learning [22.100386288615006]
We introduce a novel method to accelerate iterative solvers for physical systems with graph networks.
Unlike existing methods that aim to learn physical systems in an end-to-end manner, our approach guarantees long-term stability.
Our method improves the run time performance of traditional iterative solvers.
arXiv Detail & Related papers (2020-06-06T15:48:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.