ARYABHAT-1: India's First Fully Analog AI Processor
Designed & Built in India — A Fully Analog, Energy-Efficient, Performance-Scalable AI Processor
Key Features
- 8-Core Fully Analog Architecture
- Technology & Energy Scalable Design
- Custom Compiler for AI/ML Algorithms
- Python-based Automated Testing Framework
Technological Highlights
- Built from transistor to system level
- User-configurable energy and performance modes
- Ultra-low power consumption for edge AI tasks
- Scalable across process technologies
Bird Hotspots: Low-Power Acoustic Classification for Ecological Monitoring
Identifying Bird Habitats using TinyML & Neuromorphic Audio Processing
Key Features
- TinyML-powered low-power acoustic classification
- Neuromorphic audio front-end using in-filter computation
- Template-based SVM model requiring less data and robust to noise
- Easy deployment on low-power microcontrollers
Technological Highlights
- TI CC1352 Launchpad (ARM Cortex M4F)
- MEMS microphone for audio capture
- Real-time audio buffering and classification
- On-device bird detection logs
mmWave Radar for Activity Recognition and Localization
Real-time Multi-person Tracking for Healthcare and Surveillance
Key Features
- Radar-based Localization & Activity Recognition
- Simultaneous Multi-person Tracking
- Three-layer CNN for Real-time Classification
- Python-based GUI for Visualization & Logging
Technological Highlights
- IWR6843 mmWave radar (60–64 GHz)
- Robust in low light, dark, or foggy environments
- Privacy-preserving — uses point cloud data, no images
- Tracks range, velocity, acceleration & zone-based localization
- Classifies walking, sitting, resting, jumping, and more
Neuromorphic Ising Machine for NP-Hard Problems
A multi-institute collaboration from the Bangalore Neuromorphic Engineering Workshop (BNEW)
Key Features
- Quantum-inspired dynamics where noise aids solution exploration
- Native support for higher-order Ising interactions
- Highly scalable and hardware-efficient neuromorphic architecture
- State-of-the-art performance on benchmark problems
Technology Highlights
- Fully implemented on the RFSoC 4x2 Evaluation kit
- Fowler-Nordheim annealing provides asymptotic convergence guarantee
- Quantum-inspired algorithm on CMOS — scalable, high-speed, room-temperature operation
- Distributed, parallel, and event-driven neuromorphic computing architecture
Inspired by the way physical systems naturally evolve toward minimum-energy states, this neuromorphic Ising machine efficiently searches for optimal solutions to combinatorially hard problems — enabling fast scheduling, logistics, drug discovery, and other large-scale combinatorial optimisation tasks.
HOMI: Ultra-Fast EdgeAI Platform for Event Cameras
High-speed, low-latency AI acceleration for sparse event-based vision
Key Features
- Over 1000 FPS processing capability for HDR images
- Novel pre-processing block for generating frames from sparse events while keeping the dynamic range intact
- Energy-efficient, low-latency AI accelerator that leverages the inherent sparse nature of event data
Applications
- Navigation for Autonomous Robots
- High-Speed Drone/Missile Detection
- Space Situational Awareness (SSA) Applications
Neuromorphic Radar for Gesture Recognition
Bio-inspired, event-driven radar for always-on, low-power gesture recognition
Key Features
- Event-driven radar architecture that generates spikes only on meaningful motion — no wasteful processing of idle/background signals
- Bio-inspired asynchronous sigma-delta encoding that converts IF signals into sparse, spike-based representations mimicking retinal and auditory neurons
- Interrupt-driven processing pipeline that keeps the microcontroller in a low-power state between events
- Recognizes five distinct gestures: Push-Pull, Slow Wave, Fast Wave, Up-Down, and No Activity
Technology Highlights
- 24 GHz pulse-Doppler radar paired with a custom in-house neuromorphic sampler board
- Lightweight neural network deployed on an ARM Cortex-M0 microcontroller with a memory footprint of only ~4 KB
- Real-time inference directly on spike polarity and timestamps — no ADC, FFT, or spectrogram reconstruction required
- ≥85% real-time inference accuracy across five gestures collected from 7 users
- First demonstrated bio-inspired asynchronous sigma-delta encoding framework for radar-based HGR
By replacing continuous ADC sampling and dense spectrogram processing with sparse, event-driven sensing and computation, this neuromorphic radar framework delivers significant savings in power, memory, and latency — making it ideally suited for always-on gesture recognition in resource-constrained embedded and IoT platforms.
Asynchronous High-Speed Tracking of Astronomical Objects using Neuromorphic Cameras
Real-time, frame-free, multi-target clustering and tracking for Space Situational Awareness
Key Features
- Fully asynchronous, event-driven clustering and tracking operating directly on raw events (no frame binning)
- Constant-Turn Extended Kalman Filter (CT-EKF) with constant-neighbour search for near-constant per-event complexity
- Multi-target tracking with inherent suppression of background noise and hot pixels
- High dynamic range (≥120 dB) operation, resistant to bright-source glare and motion blur
Technology Highlights
- Deployed on Raspberry Pi 4 with a Prophesee Gen4 (IMX636) event sensor for on-device real-time inference
- Tracks blobs moving at > 17,000 px/s with per-event EKF latency < 0.7 µs
- End-to-end clustering + EKF pipeline at ~1 µs/event, < 12 px localization error, F1-score > 0.9
- Validated via LED-matrix multi-target simulations and 8-inch telescope trials (Starlink passes, drifting star fields)
By leveraging the µs-scale temporal resolution and sparse output of neuromorphic sensors, this edge-computing platform enables low-latency, low-data-rate tracking for autonomous space situational awareness on both ground- and space-based platforms.
SUSHRUT — A Reconfigurable, AI-Enabled Handheld Ultrasound Device for Super-Resolution Imaging
Portable, palmtop ultrasound with hardware-accelerated AI for high-resolution diagnostic imaging
Key Features
- Novel non-linear hardware beamforming algorithms offering superior resolution and contrast, innovated for handheld systems
- Real-time adaptability to different transducers
- 3D imaging
- Raw data extraction
- Handheld 3D sweep ultrasound
Technology Highlights
- Fast HW AI accelerator
- 128 TX/RX channels
- Embedded MCU for video generation and image processing
- Low power consumption < 6 W
- Compact 5.9 × 2.3 inch handheld/palmtop form factor
Explore More Demonstrations
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