NeuroCore - Revolutionary AI Acceleration Technology | 90x More Efficient
Introducing NeuroCore Hyperion

AI.
Faster.

The world's first Cognitive Processing Unit. 20x performance, 90x lower power.
Redefining Intelligence.

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Cognitive Power

Two revolutionary chips, one groundbreaking architecture

NeuroCore Hyperion Background
NEW LAUNCH

NeuroCore Hyperion

Datacenter-Grade Cognitive Power

AGI-level processing for immense HPC loads and datacenter deployments

FluxLIF Neurons
1,024
Synapses
2.1 Million
Performance
768-800 GSOPS
Processing Power
AGI-level performance
Scalability
Datacenter-grade
Use Cases
HPC, AI Training, Datacenters
Innovation
Next-gen cognitive architecture

AGI-Level Processing

Redefining datacenter intelligence

Massive Scale

Handles immense HPC workloads

Hyperion Performance

Unprecedented compute density

Enterprise Ready

Production-grade reliability

Interactive Architecture

The Brain
Reimagined

Explore the revolutionary layers that power NeuroCore's cognitive processing

SynaptoFlux Cognitive Matrix

Bio-synaptic fluidity meets silicon precision

CogniLive Adaptive Intelligence

Real-time learning without retraining

NeuroPulse Quantum Flow

Asynchronous pulse-driven processing

OmniScale Cognitive Core

Massively scalable architecture

NeuroCore vs Traditional Accelerators - Live Operation

NeuroCore Pulsar

2-5W
🌡️ 35°C
✓ Fanless Operation
✓ Minimal heat generation
✓ Ultra-low power consumption
✓ No active cooling required
✓ Silent operation

GPUs / TPUs / NPUs

250-400W
🌡️ 85°C 🔥
Fan 1
Fan 2
⚠️ Complex Cooling
✗ Extreme heat generation
✗ Massive power consumption
✗ Complex cooling systems required
✗ Thermal throttling issues

NeuroCore Advantage: 90x More Efficient

While GPUs, TPUs, and NPUs struggle with extreme heat generation, massive power consumption, and complex cooling requirements, NeuroCore delivers superior performance with minimal thermal output, ultra-low power draw, and passive cooling - enabling deployment anywhere from edge devices to datacenters without infrastructure redesign.

Architecture
That Thinks

NeuroCore doesn't just process, it thinks. Bio-synaptic fluidity meets silicon precision.

FluxLIF Nodes

Dynamic neurons that adapt in real-time

256 per NeuroNode

Evolving Synapses

Connections that learn and strengthen

132,000 per Node

Event-Driven Processing

Compute only when needed

Near-zero latency

Asynchronous Flow

No clock bottlenecks

90x power savings

Configurable Precision

Balance speed and accuracy

8-bit & 16-bit

Task Agnostic

One chip, infinite applications

End-to-end processing

This isn't neuromorphic.
It's the dawn of living compute.

The SynaptoFlux Cognitive Matrix (SFCM) fuses bio-synaptic fluidity with silicon precision. Dynamic FluxLIF Nodes and evolving synapses mimic the brain's genius, delivering 20x efficiency and 80% less energy than legacy AI chips.

Old AI Chips
Static, power-hungry, rigid processing
NeuroCore
Adaptive, efficient, cognitive intelligence

Revolutionary Technology

Three pillars of cognitive computing excellence

Intelligence That Evolves

CogniLive Adaptive Intelligence (CAI)

Say goodbye to static AI. NeuroCore learns on the fly—real-time, no retraining, no cloud.

Real-time learning without cloud dependency

Event-driven adaptive processing

Near-zero latency response

Continuous model improvement

Old AI chips are diesel clunkers

NeuroCore's Cognitive Processing Unit is the hyperdrive of tomorrow

Real-World Performance

Benchmark
Results

Real hardware validation across industry-standard datasets

T-Maze Decision Making

Rat's Decision Making Task using CueAccumulation Dataset

  • Testing Accuracy
  • Training Accuracy
12345678910Epoch0255075100Accuracy (%)
Final Test Accuracy
100%
Convergence
Epoch 5

Performance Highlights

On-chip learning with e-prop algorithm

Real-time adaptation without cloud connectivity

90x lower power than traditional GPUs

Event-driven processing for maximum efficiency

Technical Specifications

Learning AlgorithmCogniLive Adaptive Intelligence
Learning TypeOn-Chip Learning
Training Samples50 per epoch
HardwareNeuroCore Pulsar

Hardware Configuration

All benchmarks (T-Maze, MNIST, CIFAR-10) executed on:

NeuroCore Pulsar - Single Core IP

CogniLive Adaptive Intelligence • On-Chip Learning

Want to see more?

Download our complete benchmark suite and technical documentation

NeuroCore vs Nvidia P100

Decision Making Task - Cue Accumulation Dataset

NeuroCore Pulsar

Single Core IP

Test Accuracy100%
Power ConsumptionUltra-Low
TrainingOn-Chip
LatencyNear-Zero
GPU

Nvidia P100 16GB PCIe Server

Traditional GPU

Test Accuracy~95%
Power Consumption250W TDP
TrainingCloud-Based
LatencyHigh

Key Advantage

NeuroCore Pulsar achieves superior accuracy with 90x lower power consumption compared to traditional GPU solutions, while enabling on-chip learning without cloud dependency.

Dataset Comparison

DatasetTask TypeFinal AccuracyConvergenceTraining Time
T-Maze
Decision Making100%Epoch 5~2.5s
MNIST
Digit Recognition100%Epoch 8~3.2s
CIFAR-10
Object Recognition100%Epoch 8~4.1s
Financial
Time-Series Forecasting94%Epoch 10~115 ms

Performance That
Defies Physics

Breakthrough metrics that redefine what's possible in AI processing

18x

Performance Boost

Faster than traditional AI chips

96 GSOPS per NeuroNode

81x

Lower Power

Energy efficiency advantage

80μW per NeuroNode combined

86

GSOPS/Node

Giga Synaptic Operations per Second

Unprecedented throughput

72μW

Power Per Node

Inference + training combined

Industry-leading efficiency

NeuroNode Intelligence

Flux Neurons256
Synapses132,000
Weight Precision8-bit & 16-bit
Activation Precision8-bit & 16-bit
Latency-AccuracyConfigurable
Processing ModeTask Agnostic

Competitive Edge

vs GPUs

90x lower power consumption while maintaining superior performance

vs TPUs

Real-time adaptive learning without pre-training requirements

vs Neuromorphic

Commercial-ready with 20x higher throughput and scalability

Why Choose
NeuroCore?

The only chip that combines cutting-edge performance with revolutionary efficiency

Feature

Intel Loihi

IBM TrueNorth

AI GPUs

TPUs

Architecture

Neuromorphic
Traditional neuromorphic design with limited flexibility
Neuromorphic
First-generation neuromorphic with rigid structure
Parallel Sync
Synchronous processing with memory bottlenecks
Matrix Mult
Optimized for matrix operations only

Learning Type

Event-based
Limited on-chip learning capabilities
Static
No on-chip learning capabilities
Pre-trained
Requires offline training, no real-time learning
Pre-trained
No on-chip learning capabilities

Power Efficiency

~100 mW/core
125x higher power consumption than NeuroCore
~70 mW/core
87x higher power consumption than NeuroCore
250W - 450W+
300x higher power consumption than NeuroCore
75W - 200W
90x higher power consumption than NeuroCore

Compute Density

Moderate
8x lower compute density than NeuroCore
Slow
15x lower compute density than NeuroCore
Fast
High raw compute but inefficient for sparse neural processing
Very Fast
Fast for specific workloads, inefficient for others

Commercialization

Research only
Not available for commercial applications
Research only
Never reached commercial deployment
Cloud/HPC
Limited to data centers due to power requirements
AI Acceleration
Limited to specific cloud environments

Scalability

Limited
Difficult to scale beyond research environments
Limited
Fixed architecture with poor scaling properties
Expensive
High cost and power requirements limit scalability
Expensive
High infrastructure costs limit deployment options

Configurable Tradeoff

Not Available
Fixed architecture with minimal configurability
Not Available
No hardware-level configurability
Not Available
Software-only configuration with fixed hardware
Not Available
Fixed architecture optimized for specific workloads

Real-time Adaptation

Limited
Cannot adapt to new data without retraining
None
Static weights, no adaptation capability
None
Requires complete retraining for adaptation
None
Cannot adapt to new data without cloud retraining

Thermal Efficiency

Moderate
Requires active cooling systems
Moderate
Requires specialized cooling solutions
Poor
Requires complex cooling infrastructure
Moderate
Requires specialized cooling solutions

NeuroCore Advantages

Architecture

SynaptoFlux Cognitive Matrix

Revolutionary bio-inspired architecture with dynamic FluxLIF nodes

Learning Type

CogniLive Adaptive Intelligence

Real-time on-chip learning without retraining

Power Efficiency

80μW/Node (90x better)

Ultra-low power consumption enables edge deployment

Compute Density

96 GSOPS/Node (20x better)

Asynchronous pulse-driven processing with quantum-like efficiency

Commercialization

Production Ready

Fully commercialized with production-grade silicon

Scalability

OmniScale Architecture

Seamlessly scales from edge (4 cores) to datacenter (100+ cores)

Configurable Tradeoff

Hardware-Level Optimization

Dynamically configurable for accuracy/power/latency tradeoffs

Real-time Adaptation

Event-Driven Learning

Adapts to new data in milliseconds without retraining

Thermal Efficiency

Fanless Operation

Minimal heat generation enables passive cooling

The only chip that delivers

Commercial Readiness
Real-Time Learning
Massive Scalability
Hardware Configurability
End-to-End Processing

Infinite
Possibilities

From edge devices to datacenters, NeuroCore powers the future of intelligent computing

Edge AI & IoT

Ultra-low power intelligence for connected devices

Smart sensors
Wearables
Mobile devices

Robotics

Real-time adaptive control and decision making

Autonomous robots
Industrial automation
Collaborative robots

Automotive

Advanced driver assistance and autonomous navigation

ADAS systems
Self-driving
Vehicle intelligence

Datacenters

AGI-level processing for massive HPC workloads

AI training
Model inference
Large-scale processing

Healthcare

Intelligent medical devices and diagnostics

Medical imaging
Patient monitoring
Drug discovery

Defense

Mission-critical AI for security applications

Surveillance
Threat detection
Autonomous systems

Aerospace

Reliable AI for extreme environments

Satellite systems
Navigation
Space exploration

Industry 4.0

Smart manufacturing and predictive maintenance

Quality control
Process optimization
Predictive analytics

Edge to Cloud

NeuroCore Pulsar brings AI intelligence to the edge with ultra-low power consumption, perfect for IoT devices, wearables, and embedded systems.

Power Consumption80μW/Node
LatencyNear-zero
LearningOn-device

Datacenter Scale

NeuroCore Hyperion delivers AGI-level processing for immense HPC loads, redefining what's possible in datacenter AI deployments.

PerformanceAGI-level
ScalabilityMassive
WorkloadsImmense HPC

One Architecture.
Infinite Applications.

Massively scalable across applications and industries by configuring the number of NeuroNodes