Continual learning
Study adaptation during operation, beyond one-off offline training.
BRAND FILM / 01:15
PURE SNN · FULL GPU · CONTINUAL LEARNING
Begin with spikes.Explore another path to silicon intelligence.
Xulinghe Technology is developing a pure SNN silicon intelligence system with full-GPU engineering and continual learning as long-term research directions.
- Architecture
- Pure SNN
- Engineering
- Full GPU
- Public status
- In development
01 / OUR MISSION
Our Mission
Explore an engineering path toward silicon systems that may perceive, learn, retain state and evolve in real time.
We do not package unknowns as accomplished facts. Our public research view starts from neurons, synapses, topology and dendrite-like partitions, asking how spikes, plasticity, memory, regulation and behavior might form a verifiable system loop.
SEQUENCESPIKESILICON
Why pursue this path
Internal state
Study continuity across learning, memory and regulation, with persistence and auditability.
Event-driven computing
Explore spike computation triggered by change and the engineering potential of sparse activity.
Real-world loop
Ultimately connect perception, internal dynamics and behavior in a continuous environment.
02 / CORE RESEARCH
Two research tracks, one disclosure boundary
This site only presents public directions, status and vision. Algorithms, parameters, blueprint structure, claims and experimental secrets remain confidential.
PURE SNN SILICON INTELLIGENCE
System R&D built around pure spiking neural networks, full-GPU engineering, continual learning, long-lived state and verifiable loops. Currently in development.
Core IP strategy
A conservative intellectual-property program is in progress. This does not mean filed, accepted or granted.
320+
RESEARCH ATLAS
320+ HPP early planning baseline
320+ describes an early baseline for organizing research problems. It is not a completion count, performance metric or proof of system capability. The scope continues to evolve with the new blueprint.
03 / SYSTEM PANORAMA
System Panorama
A public-layer map of a pure SNN, full-GPU, continual-learning research system. Capabilities without runtime evidence are explicitly marked in development.
Perception
Input organization and temporal representation · In development
Spike computation
Event-driven computation and internal dynamics · In development
Learning & plasticity
Runtime adaptation and stability · In development
Memory / regulation
Long-lived state and coordinated regulation · In development
Behavior output
From internal state to verifiable output · In development
Validation / persistence
Consistency, reproducibility, save and reload · In development
FULL GPU ENGINEERING
Why full GPU
GPUs are the current engineering substrate for highly parallel updates across neurons, synapses, topology and temporal state. Full GPU is a direction, not proof of lower resource use.
- Parallel state updates
- Unified engineering substrate
- Sparse-event optimization
- Pathfinding for future hardware
SNN × LLM
Compared with LLMs: different questions, no invented benchmark
Large language models clearly lead in language, knowledge, multimodality and ecosystem maturity. The pure SNN silicon intelligence research program focuses on event-driven computation, continual learning, internal state and real-time loops. Resource or performance advantages require formal, like-for-like benchmarks.
EVIDENCE BEFORE ADVANTAGE
04 / CURRENT PROGRESS
Latest Public Progress
These statements come from the current verifiable governance layer. They describe stage status only and do not imply system capability.
A New Blueprint in Development
The work is in cross-domain evidence binding, structural consistency and audit preparation.
Final review is not complete
This does not mean implementation or runtime verification is complete, and is not a performance conclusion.
Multiple generations of R&D material
Historical versions support research review; history is not automatic proof of current capability.
Hard problems under active research
Cross-domain consistency
Keep blueprint, implementation, runtime and evidence aligned across a complex system.
Long-term stability
Maintain continuity while learning and plasticity remain active.
Verifiable closure
Connect perception, learning, memory, behavior and persistence into repeatable evidence.
05 / CONCEPT VISION
A future beyond the screen
Our long-term concept reaches embodied intelligence: seeing, hearing, communicating and acting, with perception and behavior in one continuous loop.
Concept Vision · This visual does not represent a current product or verified capability
VISION / 01
General embodied platform
A direction for multimodal perception, continual learning and coordinated action in complex environments.
Concept Vision · This visual does not represent a current product or verified capability
VISION / 02
Compact robot and robot dog
Long-term concepts for inspection, research, education, special environments and assisted living.
Concept Vision · This visual does not represent a current product or verified capabilityPotential future domains
01Robotics02Smart manufacturing03Research platforms04Industrial inspection05Special environments06Medical and rehabilitation research07Education and labs08Long-lived autonomous devices
06 / CINEMATIC FILM
Full Enterprise Film
This ten-minute film is designed for investors, researchers, and prospective partners. It presents the company, research motivation, technical direction, culture, current progress, and future applications. Future scenes are concept visions.
ENTERPRISE FILM / 10:00
Read film transcript
- Xulinghe Technology explores another possible path to silicon intelligence.
- The physical world requires continuous perception, persistent state, adaptation, and action feedback.
- Pure SNN, full-GPU engineering, and continual learning are our present research directions.
- 320+ is a research-topic planning baseline, not completed capability; the system remains in development.
- Robotics, future society, and space exploration are concept visions, not current products.
- Respect complexity. Put evidence first. Build for the long term.
RESEARCH CODE
R&D Principles
Pure SNN
No LLM or ANN substitutes for project results.
Evidence first
Claims never exceed verifiable evidence.
Long horizon
Recovery, reproducibility and continuity are core engineering problems.
Controlled disclosure
Only the public overview is shown; confidential technology and personal data remain protected.
07 / COOPERATION
Build the next stage with long-term partners
We welcome dialogue with research institutes, universities, hardware and robotics platforms, industry scenario partners, and patient capital focused on foundational intelligence. A technical brief can be shared under NDA.
COMPANY / CONTACT
Xulinghe Technology (Jiuquan, Gansu) Co., Ltd.
A legally established company focused on pure SNN silicon intelligence R&D. For research dialogue, industry cooperation, and other formal enquiries, please contact us by enterprise email.
contact@xulinghe.com
Business QQ
583598313