Spiking Neural Networks (SNNs)

Neuromorphic processing for ultra-low power, real-time intelligence.

Advantages

Why SNNs?

Beyond Traditional AI

Conventional AI at the edge is hitting a wall: power budgets, latency, and cloud dependence choke real-world deployments. Sensors capture rich signals, but silicon can’t keep up efficiently. Pulsar removes this bottleneck, delivering brain-inspired, event-driven intelligence directly at the sensor, where fast decisions actually need to happen.

The Spiking Neural Network Advantage

Ultra-low power processing enabling sensor data processing in micro- and nano-watt ranges
Real-time responsiveness with inherently low processing latency
Privacy by design through fully on-device data processing
Compact and scalable architectures optimized for embedded systems
Robust, adaptive performance resilient to noise and sparse signal activity

Benefits

Where our technology
makes a difference

Ultra-low power

Innatera’s Spiking Neural Processor architecture delivers up to 500x lower energy than conventional edge AI, enabling truly always-on sensing in battery devices.

Instant response time

Innatera’s event-driven SNNs react up to 100x – Lower latency vs traditional pipelines, turning raw sensor spikes into decisions in real time.

No cloud

Innatera’s local intelligence drastically cuts radio and cloud usage, extending battery life by orders of magnitude and keeping data private on-device.

Developer-friendly SDK

Talamo SDK lets developers import existing models, tune SNNs, and deploy to Pulsar using standard Python tools and workflows.

Architecture

Neuromorphic vs Conventional AI Architectures

Dimension

Compute paradigm

Power (inference)

Public latency claim

Comparative efficiency (vendor claim)

On-device vs cloud

Architecture blocks

Always-on sensing fit

Privacy implication

Innatera Spiking Neural Processors

Neurons don’t fire on changes alone; neurons and synapses process input data in an asynchronous manner, selectively firing only for data that is relevant to them

High-accuracy always-on processing; Activity is spatially and temporally sparse, leading to phenomanl energy efficiency gains

Sub-millisecond responsiveness

Up to 500x lower energy and up to 100x lower latency vs conventional AI solutions

Designed for real-time, on-device inference; reduces cloud dependency by processing only when needed

SNN engine + RISC-V MCU; hybrid support for SNN, CNN acceleration, and DSP

Strong fit

Local processing → less data sent to cloud by design

Conventional CNNs on mainstream digital hardware

Frame/clock-driven numerical operations (dense MACs over tensors)

Low power claims are generally at low accuracy

Often higher latency; may rely on batching or cloud offload

Baseline for comparison: energy/latency depend on platform and model size

More likely to rely on cloud or higher-end SoCs for heavy workloads

CNN accelerators/NPUs or general GPUs/CPUs optimized for dense CNN ops

Always-on possible but usually at higher energy or lower accuracy

More likely to transmit or process off-device when constrained

Innatera Spiking Neural Processors

Conventional CNNs on mainstream digital hardware

Compute paradigm

Neurons don’t fire on changes alone; neurons and synapses process input data in an asynchronous manner, selectively firing only for data that is relevant to them

Frame/clock-driven numerical operations (dense MACs over tensors)

Power (inference)

High-accuracy always-on processing; Activity is spatially and temporally sparse, leading to phenomanl energy efficiency gains

Low power claims are generally at low accuracy

Public latency claim

Sub-millisecond responsiveness

Often higher latency; may rely on batching or cloud offload

Comparative efficiency (vendor claim)

Up to 500x lower energy and up to 100x lower latency vs conventional AI solutions

Baseline for comparison: energy/latency depend on platform and model size

On-device vs cloud

Designed for real-time, on-device inference; reduces cloud dependency by processing only when needed

More likely to rely on cloud or higher-end SoCs for heavy workloads

Architecture blocks

SNN engine + RISC-V MCU; hybrid support for SNN, CNN acceleration, and DSP

CNN accelerators/NPUs or general GPUs/CPUs optimized for dense CNN ops

Always-on sensing fit

Strong fit

Always-on possible but usually at higher energy or lower accuracy

Privacy implication

Local processing → less data sent to cloud by design

More likely to transmit or process off-device when constrained

Data Journey

From Signal to Spike to Action

01 Data Capture / Sensing

Real-world signals are continuously captured from sensors, forming the foundation for intelligent, event-driven processing.

02 Encoding: Translating Signals into Spikes

Raw sensor data converts into temporal spike patterns, preserving timing and reducing unnecessary information flow.

03 Spiking Neural Processing

Event-driven SNNs analyze spikes in real time, extracting meaningful patterns with ultra-efficient compute.

04 Decoding / Actionable Output

Processed signals translate into decisions, classifications, or triggers directly at the sensor edge.

05 Feedback & Adaptation

Systems register responses over time, and can enable adaptive behavior and smarter edge intelligence in the future.

Data Capture / Sensing

Real-world signals are continuously captured from sensors, forming the foundation for intelligent, event-driven processing.

Encoding: Translating Signals into Spikes

Raw sensor data converts into temporal spike patterns, preserving timing and reducing unnecessary information flow.

Spiking Neural Processing

Event-driven SNNs analyze spikes in real time, extracting meaningful patterns with ultra-efficient compute.

Decoding / Actionable Output

Processed signals translate into decisions, classifications, or triggers directly at the sensor edge.

Feedback & Adaptation

Systems register responses over time, and can enable adaptive behavior and smarter edge intelligence in the future.

Capabilities

Designed for real-world Edge

Instant response without complex system overhead

Longer battery life, smaller thermal budgets

More intelligence per watt in edge deployments

Greater autonomy with less cloud dependency

Use Cases

Real-Life Use Cases

Human Presence Detection

Instant awareness, zero privacy trade-offs. Spiking neural processors detect human presence in real time, locally and privately, enabling smarter buildings, safer devices, and always-on sensing at microwatt power.

Gesture and Motion Recognition

Touchless control, anywhere. SNNs interpret subtle gestures and motion directly on-device, unlocking intuitive interfaces for wearables, consumer electronics, and industrial systems—without heavy compute or the cloud.

Audio Scene Recognition and Anomaly Detection

Always listening, only when needed. Event-driven spiking chips recognize keywords or unusual sounds at the edge, reacting instantly while consuming ultra-low power and preserving user privacy.

And much more.

Ecosystem

From Sensor to Intelligence
– An Ecosystem Approach

Event-driven sensing, spiking neural processing, and deployment-ready tooling form a complete path from signal to intelligent action at the edge.

Developer portal

Sensor Integration

Integrate with a growing ecosystem of sensor partners and modalities. Transform raw signals into event-based data streams that seamlessly feed into neuromorphic processing.

SNN Computation

Process event-driven data with spiking neural networks optimized for real-time, ultra-efficient inference, forming the core intelligence layer of the ecosystem.

Application Deployment

Deploy across a broad ecosystem of devices, modules, and partner solutions, enabling faster time to market for real-world edge applications.

Developer Portal

Your entry point into the ecosystem: access tools, documentation, models, and partner resources to build and deploy neuromorphic applications end to end.

Sensor Integration

Integrate with a growing ecosystem of sensor partners and modalities. Transform raw signals into event-based data streams that seamlessly feed into neuromorphic processing.

SNN Computation

Process event-driven data with spiking neural networks optimized for real-time, ultra-efficient inference, forming the core intelligence layer of the ecosystem.

Application Deployment

Deploy across a broad ecosystem of devices, modules, and partner solutions, enabling faster time to market for real-world edge applications.

Developer Portal

Your entry point into the ecosystem: access tools, documentation, models, and partner resources to build and deploy neuromorphic applications end to end.

Blogs

Embodied Intelligence II: Neuromorphic Production

Shreyas Derashri,

19/05/26

Physical AI

Embodied Intelligence: What Turing Knew in 1948 That We Are Only Now Building

Shreyas Derashri,

15/05/26

CES 2026 Signals a Shift Toward Ambient and Physical AI

Cosmin Balan,

19/01/26

Thinking in Spikes

Cosmin Balan,

28/10/25

Why neuromorphic computing can’t evolve alone

Petrut Bogdan,

28/09/25

Lessons from the floor: What global events reveal about the future of AI sensing

Katarzyna Kozdon,

05/08/25

The Invisible Revolution: How Edge AI is Redefining Intelligence in a Post-cloud Era

Cosmin Balan,

25/03/25

Ambient Intelligence: The Next Frontier of Computing

Cosmin Balan,

20/12/24

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.