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Machine Learning Services
Edge ML application using compact ML models that run on low-power embedded hardware
AI Model Development

AI Model Development

Custom ML model development using state-of-the-art algorithms and frameworks

  • Deep Learning Models
  • High-quality RTL coding using Verilog/SystemVerilog with industry best practices
  • Classification with KNN and SVG
  • Clustering using K-MEANS
  • Dimensionality Reduction using
  • PCA and UMAP
  • Supervised learning
  • Unsupervised learning
  • Ensemble Methods
  • Model evalution using confusion matrix
Deep learning framework integration

Deep learning framework integration

Tools to make AI work on small, efficient devices

  • TensorFlow, TFLite & TensorFlow RT
  • Mathematical proof-based verification for critical design properties
  • ONNX Runtime
  • PyTorch & TorchScript
  • TinyML for MCUs
Computer vision & NLP

Computer vision & NLP

computer vision and NLP solutions for real-time image devices and wearable devices

  • Object Detection and Classification
  • Comprehensive verification using advanced methodologies and tools
  • Clustering
  • Motion Detection & Tracking
  • Pose estimation using custom model
  • Key word detection
Generative AI

Generative AI

Generative AI and large language models for devices.

  • Recurrent neural networks (RNNs)
  • Structured approach to verification with comprehensive planning and metrics
  • Transformers
  • Generative adversarial networks
  • Tokenization techniques using NLTK
  • Pipelines with PyTorch data loaders
Technologies, Frameworks & Platforms
Leveraging cutting-edge ML technologies and frameworks
TensorFlow Pytorch Keras OpenCV TensorRT ONNX TinyML TensorFlow Lite Vitis AI Nvidia Riva SDK Docker Kria K26 SOM Nvidia Jetson Xavier Xilinx Zynq WIO terminal
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