Moku Neural Network

Moku Neural Network

 

The only FPGA-based neural network integrated into a full suite of test and measurement instruments. Run real-time, powerful machine learning algorithms in line with your experimental setups using Moku:Pro. Build and train models using Python, then deploy to your test systems using Moku:Pro to achieve low-latency inference and react quickly to changing experimental conditions.

 

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Key benefits

Optimize experiments with real-time machine learning, dynamic decision-making, and more efficient data processing.

User Interface of configuring the Moku Neural Network in Moku Multi-instrument mode

Make data processing fast, easy, and intelligent

Train your network to identify specific signal anomalies, trigger occurrences, beam position, and more. When the signals you're working with are difficult to define or require extensive post-processing to analyze, use the Moku Neural Network to perform intelligent real-time analysis.
Engineer configuring the software app for the Moku Neural Network on his computer

The only test device with a built-in, cost-effective neural network

Built on a powerful FPGA, the Moku Neural Network is a flexible, powerful implementation deployed inline with your other Moku test instruments. Process large datasets in a short amount of time, adjust to changing signal parameters, and optimize test configurations.
Moku Multi-instrument mode interface, with 3 slots configured with the digital filter box, neural network, and oscilloscope

Implement real-time, closed-loop feedback systems

Take in sensor data, an actuator position, or another input signal, and map it to the Moku Neural Network. Then, output an action. Many systems require inputs that are difficult to determine in advance for a particular control action — the Moku Neural Network intelligently analyzes the inputs to provide fast, effective feedback.
Lab setup with two Moku:Pro's

Make machine learning accessible and effective

The Moku Neural Network is engineered to optimize lab setups for every researcher, not just deep learning experts. With intuitive examples, fast training times, and real-time network execution, integrating a neural network into your lab has never been easier.
Engineer with Moku:Lab, running Python's API on her computer screen

Engineered to work seamlessly with your preferred APIs

Moku features API support for Python, MATLAB, LabVIEW, and more, allowing for straightforward integration of Moku with your technology stack.
MEMs (Micro-electromechanical systems) testing lab setup

Engineered for demanding applications

Closed-loop control

Noise filtering

Signal classification

Quantum emitter control

Anomaly detection

Integrates seamlessly with your tech stack

FAQ

What is a neural network?

A neural network is a machine learning structure inspired by the human brain. It consists of interconnected units called neurons, grouped together in layers: an input layer, one or more hidden (internal) layers, and an output layer. In a fully connected model like the Moku Neural Network, each neuron has a series of weights indicating how much the value or any neuron in the previous layer affects its output. These weights are adjusted during a “training” phase so the values at the output layer match a known set of corresponding inputs. After training, the model can be used for “inference,” to infer a set of output values given some inputs that it may or may not have seen before.

What is the difference between an FPGA-based neural network and a traditional neural network?

Neural networks are typically built and run on combinations of CPUs and/or GPUs. This approach gives incredible computing power, but it is also resource-intensive. Large AI models are energy hungry and often excessive for many types of signal processing applications. The flexibility and real-time processing of FPGAs makes them strong candidates for implementing small-scale neural networks. Their parallel processing capabilities benefit the linear algebra and other complex mathematics involved in the propagation of information through the network.

How do I train the Moku Neural Network?

To train a model for the Moku Neural Network, follow this example. It walks you through how to configure your model, generate training data, train the model, and export the resulting weights and biases as a .linn model to be uploaded to the Moku Neural Network.

Do I need to be a machine learning expert to use the Moku Neural Network?

Not at all — this versatile instrument is engineered to be accessible to all researchers. While machine learning experts can take Moku Neural Network applications to the next level, it’s easy for all Moku:Pro users to set up a neural network.

Featured resources

From detailed use case walkthroughs to comprehensive application notes that explore instrument features and functions, we offer a range of resources to help you work smarter and achieve more with the Moku Neural Network.

Learn what Moku can do for you

Ready to experience how the Moku Neural Network can improve your workflow so you can achieve more, faster?
It’s easy to configure Moku:Pro.