Join us for hands-on training at SPIE Photonics West

This year, we’re hosting hands-on training for current and prospective Moku users at Photonics West. Join us to hear expert tips and strategies to work more efficiently with reconfigurable, FPGA-based instrumentation. Choose from the following topics:

  • 10 a.m.: Moku 101: Get started with reconfigurable, FPGA-based instrumentation.
  • 11 a.m.: Neural Network: Develop machine learning for real-time signal processing.
  • 12 p.m.: Moku 101: Get started with reconfigurable, FPGA-based instrumentation.
  • 1 p.m.: Python and MATLAB: Use Moku APIs for automated experiments.
  • 3 p.m.: Custom FPGA programming: Code, compile, and deploy tools to Moku Cloud Compile, including a boxcar averager demo.

Event details
Wednesday, January 29, 2025
SPIE Photonics West Exhibition | South Lower Mezzanine, Room 74
The Moscone Center | San Francisco, California, USA

See below for more details on each training session. Space is limited, so register now to secure your spot and enter for a chance to win a fully loaded Moku:Go.

Getting started with reconfigurable, FPGA-based instrumentation: Moku 101

Location: Room 74, South Lower Mezzanine

Time: 10 a.m. and 12 p.m., Wednesday, January 29

Abstract

Traditional optical and electronic systems often rely on standalone test devices, leading to challenges such as instability, noise, and complexity. Flexible, digitally implemented instruments offer a powerful solution for optimizing control and characterization systems with enhanced speed and precision. In this session, we present innovative strategies for modernizing experimental setups with agile, reconfigurable, FPGA-based technology through Moku devices. Register to:

  • Understand the advantages of real-time digital signal processing for photonic and electronic experiments.
  • Leverage flexible, FPGA-based processing with a digital-first approach to quickly adapt instrumentation to your measurement requirements.
  • Build sophisticated signal-processing pipelines with multi-instrument capabilities.
Register

Neural Network development for real-time signal processing

Location: Room 74, South Lower Mezzanine

Time: 11 a.m., Wednesday, January 29

Materials: Moku:Pro devices will be provided. Please bring your laptop.

Abstract

In this session, learn to deploy a real-time, FPGA-based neural network in-line with other Moku test and measurement instruments. Build, train, and run a signal analysis autoencoder to denoise arbitrary input signals in real time. This session will be a hands-on tutorial walking through Neural network configuration in Python, deploying to Moku, and applying machine learning algorithms to physical systems for real-time data analysis. 

Sign up to learn how to:

  • Access the Moku Neural Network, which offers five dense layers of up to 100 neurons each, and five different activation functions.
  • Generate training data, train your neural network, and run it in real time in Multi-instrument Mode on Moku:Pro.
  • Build a neural network with ready-to-use Python notebooks, including the example shared in this presentation.
Register Download quick start guide

Using Python and MATLAB for automated experiments using Moku APIs

Location: Room 74, South Lower Mezzanine

Time: 1 p.m., Wednesday, January 29

Materials: Moku:Pro, Moku:Lab, and Moku:Go devices will be provided. Please bring your laptop.

Abstract

Automating control of experiments is critical for efficient data taking and repeatable results. To this end, Python has become the programming language of choice for an expansive list of research fields, owing to its ease of use and abundant supporting resources. Likewise, MATLAB is also widely used for experimental control and analysis due to its powerful processing capabilities and toolboxes.

During this hands-on training session, we will explain how to use Python and MATLAB to implement an experimental control stack with Moku, a family of reconfigurable, FPGA-based instruments, to maximize efficiency and speed. You’ll learn how to leverage Python and MATLAB for streamlined connection, control and data viewing, which will allow you to easily integrate Moku devices into your experimental stack, alongside other necessary components.

Sign up to learn how to:

  • Install the Python and MATLAB APIs for Moku.
  • Connect to and configure your Moku device using the API commands.
  • Control instrument settings and retrieve data collected from the instrument.
Register

Designing and deploying custom tools: How to use Moku Cloud Compile

Location: Room 74, South Lower Mezzanine

Time: 3 p.m., Wednesday, January 29

Materials: Moku:Pro, Moku:Lab, and Moku:Go devices will be provided. Please bring your laptop.

Abstract

Learn to develop and deploy custom functions in minutes with Moku Cloud Compile, making FPGA programming simple with the ability to implement custom functionality on Moku devices. Moku Cloud Compile enables you to run a user-programmable FPGA alongside advanced test and measurement instrumentation to stimulate, analyze, or augment your custom design. In this session, we will walk through a variety of custom functions deployable to Moku Cloud Compile, including simple arithmetic, complex math functions like a square root function, and finally a boxcar averager. We will also demonstrate how to deploy the boxcar averager to the Moku FPGA using a prebuilt bitstream, then customize the boxcar parameters to fine-tune the result. Sign up to learn how to:

  • Customize existing VHDL examples without the typical overhead of FPGA programming.
  • Build unique signal pocessing pipelines and analyze results in real time.
  • Implement a real-time, custom boxcar averager for SNR improvment, and adjust and understand boxcar parameters.
Register Download quick start guide