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Neuromorphic Intelligence

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Neuromorphic Intelligence simulates the neural structure and cognitive principles of the brain to enable computational systems with human like perception, reasoning, and learning.

Much like bees that perform complex navigation, nesting, and foraging with a million neuron mini brain at just 0.1 milliwatts, the human brain is far more complex. It transmits signals through vast neural synapses, featuring sparse activation and massive parallelism, which lays the foundation for low power and scalable computing.

As the core technology of neuromorphic intelligence, neuromorphic chips use neurons and synapses as basic units to emulate the functions, signaling, and learning of biological neurons. These chips enable low power perception, learning, memory, and decision making, overcoming the energy and efficiency limitations of traditional von Neumann architectures.

Traditional Computing Bottlenecks

Continuous Sampling → Data Explosion

  • All channels always-on
  • Data volume grows exponentially with channel count
  • Storage, transmission, and power consumption all rise
Data Transformation → Energy Inefficiency

  • Traditional computing follows: acquisition → buffer → memory → processor → write-back
  • Massive data movement drives up energy consumption
Batch Processing → Latency Accumulation

  • Acquisition, transmission, and computation are sequential
  • Latency stacks up across stages

Features of Neuromorphic Chips

New Computing Mechanism

Event-driven computing that is based on sparse communication

New Architecture

Novel synchronize computing, distributed kernel/memory

Cutting Edge Algorithm

Spatial temporal computing using spiking neural network

Neuromorphic Computing: Far More Efficient Than Traditional Paradigms

Breakthrough in architecture and algorithm

Architecture Innovation

Traditional
Architecture		Neuromorphic
Architecture
Encoding + Algorithms + Programming
Online Learning
von Neumann ArchitectureSynchronous Parallel Processing
Single Sensor Multimodality Sensor
Traditional
Architecture
Weak AI
von Neumann Architecture
Single Sensor
Neuromorphic
Architecture
General AI
Synchronous Parallel Processing
Multimodality Sensor

Algorithm Creation

Data intelligence driven by model learningCognitive bio-inspired neuromorphic intelligence
Massive data, High quality labelingFew-shot learning, labeling
Low adaptive ability, highly dependency on model.Unsupervised learning, adaptive ability
High computational costs, Power hungryLow computational resources costs, Low Power
Weak dependency on temporal sequence Strong temporal sequence dependency, general solution to classical application scenario.

Data intelligence driven by model learning
Massive data, High quality labeling
Low adaptive ability, highly dependency on model.
High computational costs, Power hungry
Weak dependency on temporal sequence
Cognitive bio-inspired neuromorphic intelligence
Few-shot learning, labeling
Unsupervised learning, adaptive ability
Low computational resources costs, Low Power
Strong temporal sequence dependency, general solution to classical application scenario.

Advantages

Event-driven

Power consumption reduced by 100-1000 times

Asynchronous

Real-time increased by 10-100 times

High temporal sequence dependency

Dynamic information processing

Cost Optimization

5–10× Improvement in System Cost

Continuous Innovation with cloud-edge fusion solution

2021–2022

Sensor Node I

Low computational costs on the edge

AI computation nodes

Smart Home
Smart Toy
CHIPS – SPECK, XYLO,
2022–2023

Sensor Node II

High computational costs on the edge

Smart Security
CHIPS - DYNAP-CNN,
2023

Sensor Fusion

Multi-sensory fusion computing

Autonomous Driving

High-speed autonomous obstacle avoidance

Vehicle-road coordination

Drones
CHIPS - DVS-SLAM,
2023–2024

Edge Cloud

Analog computation, neuromorphic near-memory computing

AR/VR

Machine Perception Optical Flow Localization Visual Navigation Control

Robots
CHIPS - MULTI-CORE DYNAP, DYNAP-M, XYLO-M,
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