Spiking Neural Processor Pulsar
The world's first neuromorphic microcontroller for the sensor edge
Pulsar is the first neuromorphic microcontroller built for real-time intelligence at the sensor edge. Delivering brain-like efficiency in a milliwatt power envelope, it enables always-on, responsive devices across wearables, IoT, and industrial systems.
Mimicking the software and hardware of the brain: Spiking Neural Networks (SNNs)
Where our technology makes a difference
Always-on intelligence without draining battery
Pulsar delivers real-time, event-driven intelligence directly at the sensor, enabling sub-millisecond response at microwatt power. Always-on, high-performance inference means no trade-off between accuracy and energy use. Application processors stay asleep until needed, extending battery life for human detection, gesture recognition, and environmental sensing; all without cloud dependence.
Smarter sensors for the edge
Embed intelligence directly into sensors. Pulsar combines SNN and DNN inference with an integrated CPU in a single chip, reducing system cost and complexity. Ultra-low energy use, short latency, and a compact 2.8 × 2.6 mm footprint enable faster development cycles and scalable deployment across edge applications.
Development made easy with Talamo SDK
Build and deploy efficiently with a complete neuromorphic toolchain. Talamo SDK lets developers create SNN models or port TensorFlow and PyTorch workloads with ease – from training to hardware deployment. No specialist expertise required.
Build your model journey with Talamo SDK
Pulsar brings real-time, event-driven intelligence directly to your devices, enabling sub-millisecond responsiveness at microwatt power levels.
Discover how Pulsar can power your innovation
Heterogeneous AI Compute
Purpose-built architecture for real-world sensing workloads Low-power Spiking Neural Network (SNN) accelerators 32-bit RISC-V CPU with floating point support 32 MAC CNN accelerator + FFT/iFFT accelerator Event-driven, sparse processing for temporal data
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Real-Time Memory & Data Flow
Optimized for low-latency sensor processing 384 KB embedded SRAM for fast data access 128 KB dedicated CNN memory 32 KB retention SRAM for low-power states DMA + scatter-gather support for efficient data movement
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Deployment-Ready Edge System
Built for integration into real-world products 2.8 × 2.6 mm WLCSP compact package Industrial operating range: -40°C to 125°C System frequency up to 160 MHz Rich I/O: QSPI, I2C, UART, I2S, GPIO, ADC
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Real-World use cases
Consumer electronics
- Speech/audio recognition
- Audio processing
- Human presence detection & recognition
- Gesture recognition
Sensor inputs: camera, microphone, radar
IoT & Smart Home
- Presence and motion sensing
- Ambient audio and anomaly detection
Sensor inputs: camera, radar, infrared
Industrial IoT
- Predictive maintenance
- On-board perception for autonomous operation
Sensor inputs: IMU, microphone
Wearables
- ECG pattern analysis
- IMU-based motion analysis
- Fall and anomaly detection
Sensor inputs: ECG, PPG, EMG
Neuromorphic vs. Conventional AI
Pulsar
Leading AI deployments
Over 100x lower
Energy per inference
Over 33x lower
Model size
Audio scene classification
33x lower
Energy per inference
1.4x shorter
Inference latency
4x smaller
Model size
Sound recognition (e.g. KWS)
42x lower
Energy per inference
177x shorter
Inference latency
30x smaller
Model size
Radar gesture recognition
Neuromorphic vs. Conventional AI
Pulsar
Leading AI deployments
Audio scene classification
Over 100x lower
Energy per inference
Over 33x lower
Model size
Sound recognition (e.g. KWS)
33x lower
Energy per inference
1.4x shorter
Inference latency
4x smaller
Model size
Radar gesture recognition
42x lower
Energy per inference
177x shorter
Inference latency
30x smaller
Model size
Driving Edge AI Innovation
Awards & Recognitions:
Embedded World Start-up Award
AI Hardware Summit
SSCC 2025
Computex
Integrate your Pulsar chip today
Discover how Pulsar’s neuromorphic architecture can transform your next-generation devices with real-time intelligence and ultra-efficient performance at the edge.