# AI News, Memristors power quick-learning neural network

## Memristors power quick-learning neural network

The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present.

The research team that created the reservoir computing system, led by Wei Lu, professor of electrical engineering and computer science at the University of Michigan, recently published their work in Nature Communications.

Reservoir computing systems, which improve on a typical neural network's capacity and reduce the required training time, have been created in the past with larger optical components.

In this process of what's called supervised learning, the connections between nodes are weighted more heavily or lightly to minimize the amount of error in achieving the correct answer.

For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.

When a set of data is inputted into the reservoir, the reservoir identifies important time-related features of the data, and hands it off in a simpler format to a second network.

This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.

Using only 88 memristors as nodes to identify handwritten versions of numerals, compared to a conventional network that would require thousands of nodes for the task, the reservoir achieved 91 percent accuracy.

To train a neural network for a task, a neural network takes in a large set of questions and the answers to those questions.

In this process of what’s called supervised learning, the connections between nodes are weighted more heavily or lightly to minimize the amount of error in achieving the correct answer.

For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.

This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.

University of Michigan researchers created a reservoir computing system that reduces training time and improves capacity of similar neural networks.

This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.

Using only 88 memristors as nodes to identify handwritten versions of numerals, compared to a conventional network that would require thousands of nodes for the task, the reservoir achieved 91% accuracy.

“We could actually predict what you plan to say next.” In predictive analysis, Lu hopes to use the system to take in signals with noise, like static from far-off radio stations, and produce a cleaner stream of data.

Reservoir computing using dynamic memristors for temporal information processing AbstractReservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space.

We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition.

We show experimentally that even a small reservoir consisting of 88 memristor devices can be used to process real-world problems such as handwritten digit recognition with performance comparable to those achieved in much larger networks.

A similar-sized network is also used to solve a second-order nonlinear dynamic problem and is able to successfully predict the expected dynamic output without knowing the form of the transfer function.

Indeed, adding vertical scan can improve the classification accuracy to 92.1% as verified through simulation using the device model, although the system also becomes larger and requires 672 inputs.

The computing capacity added by the memristor-based reservoir layer was analyzed by comparing the RC system performance with networks having the same connectivity patterns, by replacing the reservoir layer with a conventional nonlinear downsampling function.

For the second-order dynamic problem that is more naturally suited for the RC system, our analysis shows that the small RC system significantly outperforms a conventional linear network, with orders-of-magnitude improvements in prediction NMSE.

The demonstration of memristor-based RC systems will stimulate continued developments to further optimize the network performance toward broad applications in areas, such as speech analysis, action recognition and prediction.

Future algorithm and experimental advances that can take full advantage of the interconnected nature of the crossbar structures, by utilizing the intrinsic sneak paths and possible loops in the system may further enhance the computing capacity of the system.

## Memristors to Power Quick-Learning Neural Networks

Imagine a class full of artificially intelligent machines (let’s say robot doctors) that have attended a lecture, on a new procedure of conducting surgery.

As a key concept in training machines to think like humans -as in, without prior programming, neural networks are the new target for research and improvement.

Now, the team is using memristors chip -which ideally requires minimal space -and can also be integrated  straightforwardly and fast into pre-existing silicon-based electronics.

Technically, this contrasts with usual computer systems, whereby processors execute logic separate from memory modules.  Wei Lu and team employed a special memristor with abilities to memorize events –especially those in the near future.

For instance, a system can bring up the exact photo when asked to identify a human face, this is because it has learned the distinct features of human faces from the photos providing during the training session.

The interesting part is that reservoir-computing systems that use memristors can skip those expensive training processes and still give the network the capability to remember details with over 98 percent accuracy.

After a set of data is inputted, the reservoir identifies vital time-related features of the data, then hands it off in a new simpler format to the next network in-line.

Now, it is this second network that will require a bit of training to ideally alter the weights of the features and outputs availed on the first network until it attains an acceptable level of error.

## Reservoir Computing: Harnessing a Universal Dynamical System

Gauthier There is great current interest in developing artificial intelligence algorithms for processing massive data sets, often for classification tasks such as recognizing a face in a photograph.

dynamical system to predict the dynamics of a desired system is one approach to this problem that is well-suited for a reservoir computer (RC): a recurrent artificial neural network for processing time-dependent information (see Figure 1).

While researchers have studied RCs for well over 20 years [1] and applied them successfully to a variety of tasks [2], there are still many open questions that the dynamical systems community may find interesting and be able to address.

Mathematically, an RC is described by the set of autonomous, time-delay differential equations given by $\frac{dx_i}{dt} = -\gamma_i x_i + \gamma_i f_i \big[\sum\limits_{j=1}^j W^{in}_{i,j}u_j(t)+ \sum\limits_{n=1}^N W^{res}_{i,n}x_n (t - \tau_{i,n}) + b_i \big], \\y_k(t) = \sum\limits_{m=1}^N W^{out}_{k,m} \mathcal{X}_m,\: \: \: \: \:i = 1, ..., N \: \: \: \: \: k = 1, ..., K, \tag1$ with $$J$$ inputs $$u_j$$, $$N$$ reservoir nodes $$x_i$$,and $$K$$ outputs with values $$y_k$$.

Here, $$\gamma_i$$ are decay constants, $$W^{in}_{i,j} (W^{res}_{i,n})$$are fixed input (internal) weights, $$\tau_{i,n}$$ are link time delays, $$b_i$$ are biases, and $$W^{out}_{k,m}$$are the output weights whose values are optimized for a particular task.

We can solve $$(2)$$ in a least-square sense using pseudo-inverse matrix routines that are often included in a variety of computer languages, some of which can take advantage of the matrices&rsquo;

We can also find a solution to $$(2)$$ using gradient descent methods, which are helpful when the matrix dimensions are large, and leverage toolkits from the deep learning community that take advantage of graphical processing units.

Furthermore, we can utilize the predicted time series as an observer in a control system [4] or for data assimilation of large spatiotemporal systems without use of an underlying model [6].

The following is an open question: how can we optimize the parameters in $$(1)$$ and $$(2)$$ to obtain the most accurate prediction in either the prediction or classification tasks, while simultaneously allowing the RC to function well on data that is similar to the training data set?

Early studies focused on the so-called echo state property of the network&mdash;where the output should eventually forget the input&mdash;and the consistency property, where outputs from identical trials should be similar over some period.

However, this scenario ignores the input dynamics and is mostly a statement of the stability of $$\mathbf{X}= 0$$.Recent work is beginning to address this shortcoming for the case of a single input channel, demonstrating that there must be a single entire output solution given the input [5].

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