LYCHIP: Perceive as Compute The Next-Generation Edge Intelligence Arc
LYCHIP: Perceive as Compute
The Next-Generation Edge Intelligence Architecture
Core Philosophy
- Architectural Innovation: Breaks the traditional "Sensor → Processor → Memory" linear pipeline
- Zero Data Movement: Feature extraction and preliminary decision-making at the perception source
- Event-Driven Computing: Computation triggered only by valid information, achieving ultra-low power consumption
Technical Implementation Path
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Traditional Architecture:
[Sensor] → ADC → Memory → CPU/NPU → Result Output
↑Data Movement↓ ↑Power Bottleneck↓
LYCHIP Architecture:
[Smart Sensor] → On-Chip Feature Extraction → Dynamic Decision → Result Output
↑Perceive as Compute↓ ↑90% Power Reduction↓Three Core Technologies
- Near-Sensor ComputingIntegrated ADC and preprocessing unitPreliminary feature extraction in the analog domainSupports multi-modal fusion (optical, acoustic, imaging)
- Dynamic Compute FlowEvent-triggered wake-up mechanismOn-demand dynamic precision adjustmentIntelligent background scene suppression
- Hierarchical Decision NetworkL1: Real-time response at sensor (µs-level)L2: On-chip edge decision (ms-level)L3: Cloud-edge collaborative optimization (scene-adaptive)
Performance Comparison
| Scenario | Traditional Solution | LYCHIP Solution | Improvement |
|---|---|---|---|
| Face Detection Wake-up | 120mW | 8mW | 93% Power Reduction |
| Voice Keyword Recognition | 85mW | 6mW | 92% Power Reduction |
| Industrial Anomaly Detection | 210mW | 15mW | 92% Power Reduction |
| Response Latency | 15-30ms | 0.5-2ms | 90% Latency Reduction |
Application Scenarios
🔹 Smart Security
- Human detection triggers recording, zero upload of invalid video
- Perimeter protection: integrated vibration sensing + image verification
🔹 Industrial IoT
- Equipment vibration anomaly sensing and fault prediction
- Visual product quality screening (only defects trigger recording)
🔹 Consumer Electronics
- True wireless earbuds: integrated voice wake-up + noise cancellation
- Smartwatches: dynamic heart rate monitoring (only anomalies reported)
🔹 Autonomous Driving
- Multi-sensor fusion perception and decision-making
- Critical event priority processing mechanism
Development Support
- LY-Sense SDK: One-click configuration for perception tasks
- Simulator: Supports perception-computation co-simulation
- Algorithm Marketplace: 50+ pre-built Perceive-as-Compute modelsFace perception model (1mW @ detection)Voice wake-up model (0.8mW @ recognition)Vibration pattern recognition model (2mW @ classification)
Technology Roadmap
- 2024: LYCHIP Gen1 (6nm, 16TOPS)
- 2025: Integrated silicon photonics perception unit
- 2026: R&D on quantum sensing-computation fusion architecture
Core Value Proposition
- Ultimate Power Efficiency: Eliminates redundant data movement, reduces power by an order of magnitude
- Real-Time Response: End-to-end perception-to-decision latency <2ms
- Privacy & Security: Raw data remains at the sensor; only features/decisions are transmitted
- Bandwidth Savings: Effective data volume reduced by >95%
LYCHIP Perceive as Compute — Redefining the starting point of edge intelligence, ensuring every perception generates direct value.