{"id":20426,"date":"2024-10-23T05:03:13","date_gmt":"2024-10-23T05:03:13","guid":{"rendered":"https:\/\/liquidinstruments.com\/?page_id=20426"},"modified":"2026-01-13T21:58:00","modified_gmt":"2026-01-13T21:58:00","slug":"neural-network","status":"publish","type":"page","link":"https:\/\/liquidinstruments.com\/neural-network\/","title":{"rendered":"Neural Network"},"content":{"rendered":"

[vc_row content_placement=”middle” css=”.vc_custom_1729822762524{background-image: url(https:\/\/liquidinstruments.com\/wp-content\/uploads\/2024\/10\/blob-white.png?id=21218) !important;}”][vc_column]\n

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\n \"Moku<\/div><\/div>\n
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Moku Neural Network<\/h1>\n

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. Build and train models using Python, then deploy to your test systems using Moku to achieve low-latency inference and react quickly to changing experimental conditions.<\/p>\n\n <\/div>\n <\/div>\n

\n Download datasheet<\/span><\/a>\n \n \n \n <\/div>\n <\/div>\n <\/div>
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Neural network examples<\/h2>\n \n <\/div>\n \n \n
\n \"Engineer<\/div>

How to build a neural network<\/h3>First, download Python and install Keras for Tensorflow. Build and train your network on data captured from your Moku or from simulation. Once you're happy with your model performance, upload the weights and biases to your Moku for low-latency inference.<\/span><\/div><\/a>\n<\/sl-carousel-item>\n
\n \"Moku<\/div>

Signal autoencoder<\/h3>Use the Moku Python API to generate noisy data using your Moku device. Next, train your network on the data. Then use the Moku Neural Network to filter dynamic and unpredictable noise out of an input signal.<\/span><\/div><\/a>\n<\/sl-carousel-item>\n
\n \"User<\/div>

Signal classification<\/h3>This is one of the most common neural network applications. Input data is mapped to a probability distribution across different classes. Build complex pass\/fail criteria in your test, react quickly to changing experimental conditions, or catalog and track the occurrence of expected or anomalous waveforms.<\/span><\/div><\/a>\n<\/sl-carousel-item>\n
\n \"Moku:Lab<\/div>

Quadrant photodiode sensing<\/h3>Precisely measure beam position on your quadrant photodiode (QPD) with minimal calibration. Learn and correct for lens distortions and misalignments in real time, with low latency that won't affect your controller stability. Training data can be generated and captured with your Moku, or built from simulations of your optical setup.<\/span><\/div><\/a>\n<\/sl-carousel-item>\n
\n \"All<\/div>

See more examples<\/h3>Download and deploy more pre-built Neural Network models including signal identification, a signal generator, and more.<\/span><\/div><\/a>\n<\/sl-carousel-item>\n <\/sl-carousel>\n <\/div>[\/vc_column][\/vc_row][vc_row css=”.vc_custom_1730434790184{background-image: url(https:\/\/liquidinstruments.com\/wp-content\/uploads\/2024\/11\/blob-colour.png?id=21324) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column]
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Key benefits<\/h2>\n

Optimize experiments with real-time machine learning, dynamic decision-making, and more efficient data processing.<\/p>\n <\/div>\n \n

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\n \"User<\/div>

Make data processing fast, easy, and intelligent<\/h3>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.<\/span><\/div><\/div>\n<\/sl-carousel-item>\n
\n \"Engineer<\/div>

The only test device with a built-in, cost-effective neural network<\/h3>Built on a powerful FPGA, the Moku Neural Network is a flexible, powerful implementation deployed inline with your other Moku test instruments.<\/span><\/div><\/div>\n<\/sl-carousel-item>\n
\n \"Moku<\/div>

Implement real-time, closed-loop feedback systems<\/h3>Take in sensor data, an actuator position, or another input signal, and map it to the Moku Neural Network. Then, output an action.<\/span><\/div><\/div>\n<\/sl-carousel-item>\n
\n \"Lab<\/div>

Make machine learning accessible and effective<\/h3>With intuitive examples, fast training times, and real-time network execution, integrating a neural network into your lab has never been easier.<\/span><\/div><\/div>\n<\/sl-carousel-item>\n
\n \"Engineer<\/div>

Engineered to work seamlessly with your preferred APIs<\/h3>Automate with Python, MATLAB, and LabVIEW APIs for straightforward control of complex setups or repetitive tasks.<\/span><\/div><\/div>\n<\/sl-carousel-item>\n <\/div>\n <\/div>\n <\/div>
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<\/div>\n \"MEMs\n<\/div>

Engineered for demanding applications<\/h2><\/div>
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Closed-loop control<\/p>\n\n <\/div>

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Noise filtering<\/p>\n\n <\/div>

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Signal classification<\/p>\n\n <\/div>

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Quantum emitter control<\/p>\n\n <\/div>

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Anomaly detection<\/p>\n\n <\/div><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

FAQ<\/h2>\n

[\/vc_column_text]\n What is a neural network?<\/span>\n

A neural network<\/a> 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 \u201ctraining\u201d phase so the values at the output layer match a known set of corresponding inputs. After training, the model can be used for \u201cinference,\u201d to infer a set of output values given some inputs that it may or may not have seen before.<\/p>\n\n<\/sl-details>\n What is the difference between an FPGA-based neural network and a traditional neural network?<\/span>\n

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.<\/p>\n\n<\/sl-details>\n How do I train the Moku Neural Network?<\/span>\n

To train a model for the Moku Neural Network, follow this example<\/a>. 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.<\/p>\n\n<\/sl-details>\n Do I need to be a machine learning expert to use the Moku Neural Network?<\/span>\n

Not at all \u2014 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\u2019s easy for all Moku:Delta or Moku:Pro users to set up a neural network.<\/p>\n\n<\/sl-details>[\/vc_column][\/vc_row][vc_row][vc_column]

Featured resources<\/h2>\n<\/div><\/div><\/div>[vc_basic_grid post_type=”post” max_items=”6″ item=”13753″ grid_id=”vc_gid:1768341458063-1afac4e2-97bf-4″ taxonomies=”291″ el_class=”container”][\/vc_column][\/vc_row][vc_row][vc_column]\n
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Learn how Moku can help you<\/h2>\n

Connect with an applications engineer for a personalized demo.<\/p>\n\n <\/div>\n <\/div>\n

\n Schedule a demo<\/span><\/a>\n Shop hardware<\/span><\/a>\n \n \n <\/div>\n <\/div>\n <\/div>[\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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el_class=”container”][\/vc_column][\/vc_row][vc_row][vc_column][\/vc_column][\/vc_row]<\/p>\n","protected":false},"author":45,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/neural-network.php","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-20426","page","type-page","status-publish","hentry","site-category-instrument"],"acf":[],"yoast_head":"\nNeural Network | Liquid Instruments<\/title>\n<meta name=\"description\" content=\"Use the Moku Neural Network to run real-time machine learning experiments on FPGA-based hardware to get real time inference.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/liquidinstruments.com\/neural-network\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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