FPGA DESIGN AND EMULATION

FPGAs – soft silicon for hard problems.

We don’t leave home without one.

FPGAs are truly the wild card in the hardware engineer’s design pack. Ignitarium has used FPGAs to enable compute intensive low volume system realizations and to create quick prototypes. We have worked on emulation of complex Multimedia SoCs partitioned across multiple FPGAs. Armed with alliance partnerships of Xilinx and Intel-Altera, we are today a key partner for many a system company in their FPGA based product deployments.

Do it right with Ignitarium

FPGA based products: System Architecture to product testing

Ignitarium has deep understanding of various FPGA families and their key differentiators. We have tools for early analysis and selection coupled with years of experience working on multiple FPGA based designs. Our competencies include :

FPGA Prototypes: Enabling early design confidence

An early confidence metric that semiconductor companies look forward to, before starting down the path of a costly ASIC design, is to create a quick FPGA based prototype of their custom logic or algorithm. At Ignitarium, our combined hardware and software teams have created many ASIC prototypes using FPGAs by performing the following sub tasks:

Emulation: Accelerating software cycles

Emulation has become an integral part of the SoC design flow to enable early software development and enhance confidence on silicon. Ignitarium has expertise in emulation boxes like Palladium, Veloce and Zebu as well as mainstream FPGA-based emulation boards like HAPS and Prodesign FPGA. Our emulation expertise includes:

FPGA Emulation graphic

Case Studies

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Human Pose Detection & Classification

Some Buildings in a city

Features:

  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

  • Patient Monitoring in Hospitals
  • Surveillance
  • Sports/Exercise Pose Estimation
  • Retail Analytics

OCR / Pattern Recognition

Some Buildings in a city

Use cases :

  • Analog dial reading
  • Digital meter reading
  • Label recognition
  • Document OCR

Highlights :

  • Configurable for text or pattern recognition
  • Simultaneous Analog and Digital Dial reading
  • Lightweight implementation

Behavior Monitoring

Some Buildings in a city

Use cases :

  • Fall Detection
  • Social Distancing

Highlights :

  • Can define region of interest to monitor
  • Multi-subject monitoring
  • Multi-camera monitoring
  • Alarm triggers

Attire & PPE Detection

Some Buildings in a city

Use cases :

  • PPE Checks
  • Disallowed attire checks

Use cases :

  • Non-intrusive adherence checks
  • Customizable attire checks
  • Post-deployment trainable

 

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    Real Time Color Detection

    Use cases :

    • Machine vision applications such as color sorter or food defect detection

    Highlights :

    • Color detection algorithm with real time performance
    • Detects as close to human vison as possible including color shade discrimination
    • GPGPU based algorithm on NVIDIA CUDA and Snapdragon Adreno GPU
    • Extremely low latency (a few 10s of milliseconds) for detection
    • Portable onto different hardware platforms

    Missing Artifact Detection

    Use cases :

    • Detection of missing components during various stages of manufacturing of industrial parts
    • Examples include : missing nuts and bolts, missing ridges, missing grooves on plastic and metal blocks

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Single-pass detection of many categories of missing artifacts
    • In-field trainable neural networks with dynamic addition of new artifact categories
    • Implementation using low cost cameras and not expensive machine-vision cameras
    • Learning via the use of minimal training sets
    • Options to implement the neural network on GPU or CPU based systems

    Real Time Manufacturing Line Inspection

    Use cases :

    • Detection of defects on the surface of manufactured goods (metal, plastic, glass, food, etc.)
    • Can be integrated into the overall automated QA infrastructure on an assembly line.

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Use of consumer or industrial grade cameras
    • Requires only a few hundred images during the training phase
    • Supports incremental training of the neural network with data augmentation
    • Allows implementation on low cost GPU or CPU based platforms

    Ground Based Infrastructure analytics

    Some Buildings in a city

    Use cases :

    • Rail tracks (public transport, mining, etc.)
    • Highways
    • Tunnels

    Highlights :

    • Analysis of video and images from 2D & 3D RGB camera sensors
    • Multi sensor support (X-ray, thermal, radar, etc.)
    • Detection of anomalies in peripheral areas of core infrastructure (Ex: vegetation or stones near rail tracks)

    Aerial Analytics

    Use cases :

    • Rail track defect detection
    • Tower defect detection: Structural analysis of Power
      transmission towers
    • infrastructure mapping

    Highlights :

    • Defect detection from a distance
    • Non-intrusive
    • Automatic video capture with perfectly centered ROI
    • No manual intervention is required by a pilot for
      camera positioning