Project Overview
Key goals and development objectives
Development Context
The "Servitor" project is being developed in response to the current need for domestic hardware-software solutions for accelerating neural network computations, capable of replacing imported platforms (primarily NVIDIA) in artificial intelligence systems.
The initiative is implemented within the development of the APC "Lovchiy" and aims to ensure technological sovereignty in the field of computer vision systems for protecting critical facilities.
Direct Integration
The developed complex is planned to be directly integrated into APC "Lovchiy" to replace imported computational modules.
Main Project Goals
Import Substitution of Computing Platforms
Creating a domestic alternative to NVIDIA platforms for neural network computations
Performance Enhancement
Optimizing computations for computer vision tasks using RNS
Ensuring Technological Independence
Creating a full cycle of development and production of computing systems for AI
Adaptation to Specific Tasks
Specializing architecture for UAV detection and tracking tasks
Technological Features
Architecture and key technologies of the project
Hardware-Software Complex Architecture
Domestic Processors
Use of Russian processor solutions as the basic computing platform
Elbrus, Baikal, Skif
Residue Number System (RNS)
Application of RNS modular arithmetic for parallelizing and accelerating neural network computations
Parallel Processing
Neural Network Optimization
Specialized algorithms and architecture for efficient execution of convolution and matrix operations
YOLO, CNN, RNN
Principle of Residue Number System Operation
RNS is based on representing numbers as sets of remainders from division by coprime moduli. This allows performing arithmetic operations in parallel on each remainder, which significantly increases computation speed for operations typical of neural networks.
+
Parallelism
×
No Carry-Over
÷
High Precision
⚡
Up to 5x Acceleration
Key Technical Specifications
| Parameter | Specification | Advantage |
|---|---|---|
| Architecture | Hybrid (CPU + RNS Accelerator) | Optimized for neural network computations |
| Performance | Up to 20 TOPS (trillion operations per second) | Comparable to NVIDIA Jetson Xavier |
| Power Efficiency | 15-30 W (depending on configuration) | Suitable for field deployment |
| Supported Neural Networks | YOLO, ResNet, MobileNet, EfficientNet | Compatibility with major architectures |
| Interfaces | PCIe, USB 3.0, Ethernet, CAN | Integration with existing infrastructure |
| Operating Temperature | -40°C to +85°C | Extreme operating conditions |
Comparison with Imported Alternatives
Analysis of competitive advantages and disadvantages
Comparative Analysis: "Servitor" vs NVIDIA Jetson
| Criterion | "Servitor" | NVIDIA Jetson | Conclusion |
|---|---|---|---|
| Origin | Domestic Development | Imported (USA) | Advantage: technological independence |
| Performance | 15-20 TOPS | 10-32 TOPS (depending on model) | Comparable to mid-range models |
| Power Consumption | 15-30 W | 10-60 W | Advantage: optimized for field deployment |
| Ecosystem | Developing | Mature (CUDA, TensorRT) | Disadvantage: tool development required |
| Cost | Expected 20-30% lower than analogs | High ($400 to $2000+) | Advantage: economic efficiency |
| Support | Direct contact with developer | Corporate support | Advantage: rapid adaptation to tasks |
| Security | Full control over code and architecture | Closed architecture | Advantage: security for government sector |
Key Competitive Advantages of "Servitor"
Technological Sovereignty
Full control over the technology stack without dependence on foreign solutions
Specialization for Protection Tasks
Architecture optimized for computer vision algorithms in security systems
Adaptability to Customer Requirements
Ability to customize for specific tasks and operating conditions
Reduced Total Cost of Ownership
Lower ownership cost due to domestic production and maintenance
Development and Implementation Stages
Project roadmap for 2024-2026
Stage 1: Research
2024Theoretical research and modeling of RNS accelerator architecture. Development of algorithm prototypes for neural network computations.
Stage 2: Hardware Prototype
2025 (Q1)Creation of the first hardware prototype based on domestic processors. Integration of RNS accelerator and performance testing.
Stage 3: Software Ecosystem
2025 (Q2-Q3)Development of drivers, compilers, and libraries for developers. Creation of tools for porting existing neural networks to the "Servitor" platform.
Stage 4: Integration with "Lovchiy"
2025 (Q4)Replacement of imported computational modules in APC "Lovchiy" with the "Servitor" platform. Field trials and optimization for real operating conditions.
Stage 5: Serial Production
2026Organization of serial production of the "Servitor" platform. Expansion of application areas to other security and computer vision systems.
Conclusions and Prospects
Analytical assessment of the project and recommendations
Analytical Assessment
Technological Significance
The "Servitor" project has high strategic significance for ensuring Russia's technological sovereignty in the field of artificial intelligence and computer vision systems.
Risks and Challenges
Main risks are associated with creating a competitive software ecosystem and ensuring compatibility with existing neural network frameworks.
Market Potential
Potential market includes not only security systems but also other areas: industrial control, transportation, medicine, where efficient AI solutions are required.
Recommendations
Priority Development Directions
- Focus on optimization for specific perimeter protection tasks
- Development of tools to simplify neural network porting
- Creation of reference implementations for key algorithms
Implementation Strategy
- Phased replacement of imported components in existing systems
- Creation of demonstration zones at customer sites
- Development of training programs for integrators
Conclusion
The "Servitor" project represents a strategically important development aimed at creating a domestic hardware-software platform for accelerating neural network computations. Successful implementation of the project will not only ensure technological independence of security systems like APC "Lovchiy", but also create a foundation for the development of a whole class of Russian AI solutions in various industries and the defense complex.
Analytical report prepared based on
"Servitor" project technical documentation