Edge AI is revolutionizing the field of artificial intelligence by moving data processing from centralized cloud servers directly to local devices.
撰文：Advait Jayant、Matthew Sheldon、Sungjung Kim 和 Swastik Shrivastava
Compiled by: BeWater
With Meta’s lightweight Llama 1B and 3B parameter models optimized for on-device use cases coming online soon, and Apple Intelligence set to release its new product in late October, we believe edge AI and on-device AI will be the biggest topics of 2025.
Peri Labs and BeWater have collaborated to release a 250-page report covering:
The necessity of edge AI
Core innovations in edge AI
Why Edge AI Needs Encryption
Understanding the core framework of edge AI
The State of Edge AI and Encryption
BeWater has translated this report into Chinese, and the highlights are as follows:
The rise of edge AI
Edge AI is revolutionizing the field of artificial intelligence by moving data processing from centralized cloud servers directly to local devices. This approach addresses the limitations of traditional AI deployments, such as high latency, privacy concerns, and bandwidth limitations. By enabling real-time data processing on devices such as smartphones, wearables, and IoT sensors, edge AI reduces response times and keeps sensitive information securely on the device itself.
Technological advances in hardware and software have made it possible to run complex AI models on resource-constrained devices. Innovations such as dedicated edge processors and model optimization technology make on-device computing more efficient without significantly impacting performance.
Key Point 1: The rapid growth of AI has outpaced Moore’s Law.
Moore’s Law states that the number of transistors on a microchip doubles approximately every two years. However, the growth of AI models has outpaced hardware improvements, resulting in a widening gap between computing demand and supply. This gap makes the co-design of hardware and software essential.
Point 2: Major industry giants are increasing their investments in edge AI and adopting different strategies.
Major industry giants are investing heavily in edge AI, recognizing its ability to revolutionize fields such as healthcare, autonomous driving, robotics, and virtual assistants by providing instant, personalized, and reliable AI experiences. For example, Meta recently released a model optimized for edge devices, and Apple Intelligence will also release its edge AI technology at the end of October.
The intersection of edge AI and encryption
Point 3: Blockchain provides a secure and decentralized trust mechanism for edge AI networks
Blockchain ensures data integrity and tamper-resistance through its immutable ledger, which is particularly critical in a decentralized network composed of edge devices. By recording transactions and data exchanges on the blockchain, edge devices can securely authenticate and authorize operations without relying on centralized institutions.
Key Point 4: Cryptoeconomic Incentives Promote Resource Sharing and Capital Expenditure
Deploying and maintaining edge networks requires a lot of resources. Cryptoeconomic models or token incentives can support the construction and operation of the network by providing token rewards to encourage individuals and organizations to contribute computing power, data, and other resources.
Point 5: The DeFi model promotes efficient allocation of resources
By introducing concepts such as staking, lending, and liquidity pools in DeFi, the edge AI network is able to establish a market for computing resources. Participants can provide computing power by staking tokens, lend excess resources, or contribute to shared pools to obtain corresponding rewards. Smart contracts automatically execute these processes, ensuring that resources are allocated fairly and efficiently based on supply and demand, and implementing dynamic pricing mechanisms in the network.
Point 6: Decentralization of Trust
In a decentralized network of edge devices, it is challenging to establish trust without central supervision. In crypto networks, trust is achieved through mathematical means; this trust based on computation and mathematics is the key to enabling trustless interactions, which is not currently available in AI.
Future Outlook
Looking ahead, there are still a lot of opportunities for innovation in the field of edge AI. We will see edge AI become an integral part of our lives in many application scenarios, such as hyper-personalized learning assistants, digital twins, self-driving cars, collective intelligence networks, and emotional AI companions. We are very excited about the future!
