MathWorks > Case Studies > Cutting Algorithm Development Time with MATLAB: Q&A with FLIR

Cutting Algorithm Development Time with MATLAB: Q&A with FLIR

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Customer Company Size
Large Corporate
Product
  • MATLAB
  • HDL Coder
Tech Stack
  • MATLAB
  • HDL Coder
  • FPGA
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Productivity Improvements
  • Digital Expertise
Technology Category
  • Platform as a Service (PaaS) - Application Development Platforms
  • Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
  • Security & Public Safety
Applicable Functions
  • Product Research & Development
  • Quality Assurance
Use Cases
  • Predictive Maintenance
  • Machine Condition Monitoring
Services
  • Software Design & Engineering Services
  • System Integration
About The Customer
Founded in 1978, and with 2800 employees worldwide, FLIR Systems designs and manufactures advanced sensing technologies for applications including search and rescue, airborne and ground-based surveillance, manufacturing process control, and environmental monitoring.
The Challenge
Our hardware engineers were translating algorithms developed by algorithm engineers into HDL using written specifications, and without knowing exactly how the algorithms worked. If the FPGA implementation did not perform like our simulations, we never knew if the implementation or the algorithm was the problem. And even a small change to the algorithm meant rewriting most of the HDL.
The Solution
With MATLAB® and HDL Coder™, we can generate synthesizable HDL code directly from the algorithm. The HDL is implemented and tested on the FPGA, and the results are verified against the simulation. Our customer was ecstatic when, a few months after seeing our MATLAB simulations of a new thermal imaging filter, we showed them the first working camera with this new filter and the camera performed exactly like the simulations.
Operational Impact
  • Our algorithm developers produce FPGA prototypes on their own, cutting prototyping time significantly.
  • We increased MATLAB code reuse for HDL code generation for other projects from 0% to 30%.
  • We can make even major changes to our algorithms quickly: In just three hours, one of our engineers made a significant algorithmic change to a core filter that previously would have required six weeks.
Quantitative Benefit
  • Cutting prototyping time by up to 60%.
  • Increased MATLAB code reuse for HDL code generation from 0% to 30%.
  • Significant algorithmic changes that previously required six weeks now take just three hours.

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