On-chip probabilistic inference for charged-particle tracking at the sensor edge
Tracking charged-particles at the sensor edge, using on-chip AI inference, under strict latency, precision, and area constraints.
PQuant: Streamlining ML Model Compression to Deployment for Next-Gen Detector Systems
PQuant/PQuantML: config-driven pruning + quantization pipeline (PyTorch/TensorFlow) with hardware-aware MDMM optimization; aimed at FPGA/ASIC deployment workflows (hls4ml …
Sensor Co-design for smartpixels
Sensor/algorithm co-design studies for smart pixel filtering: geometry, magnetic field, radiation damage, and noise.
Danush shekar
In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors
In-pixel signal processing with integrated AI-based filtering for real-time data reduction in tracking detectors.
Benjamin parpillon