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Edge AI 2025 Core Technology Narrative?
Authored by: Advait Jayant, Matthew Sheldon, Sungjung Kim and Swastik Shrivastava
Compile: BeWater
With the recent launch of the lightweight Llama 1B and 3B parameter models optimized for device-side application scenarios by Meta, and the upcoming release of its new product by Apple Intelligence at the end of October, we believe that edge AI and device-side AI will be the biggest topics of 2025.
Peri Labs and BeWater have collaborated to release a report of about 250 pages, covering:
BeWater has translated this report into Chinese, and the essence of the summary is as follows:
The Rise of Edge AI
Edge AI is revolutionizing the field of artificial intelligence by directly transferring data processing from centralized cloud servers to local devices. This approach addresses the limitations of traditional AI deployment, such as high latency, privacy issues, and bandwidth constraints. By enabling real-time data processing on devices such as smartphones, wearable devices, and internet of things sensors, edge AI reduces response time and securely stores sensitive information on the devices themselves.
The advancement in hardware and software technology makes it possible to run complex AI models on resource-constrained devices. Innovations such as dedicated edge processors and model optimization techniques make on-device computing more efficient without significantly impacting performance.
Key point 1: The rapid rise of AI has already surpassed Moore's Law.
Moore's Law states that the number of transistors on a microchip doubles roughly every two years. However, the rise speed of AI models has already outpaced the pace of hardware improvements, leading to a widening gap between computing demand and supply. This gap makes it essential for hardware and software to be co-designed.
Point 2: Major industry giants are increasing their investment in edge AI and adopting different strategies.
Major industry giants are investing heavily in edge AI, recognizing its potential to completely transform fields such as healthcare, autonomous driving, robotics, and virtual assistants by providing instant, personalized, and reliable AI experiences. For example, Meta recently released models optimized for edge devices, and Apple Intelligence will also launch its edge AI technology at the end of October.
The Intersection of Edge AI and Encryption Technology
Key Point 3: Blockchain provides a secure, Decentralization trust mechanism for edge AI networks
Blockchains ensure the integrity and tamper resistance of data through their immutable ledger, which is particularly crucial in Decentralization networks composed of edge devices. By recording transactions and data exchanges on-chain in the blockchain, edge devices can securely perform identity verification and authorization operations without relying on centralized institutions.
Point 4: encryption economic incentive mechanism promotes resource sharing and capital expenditure
Deploying and maintaining edge networks requires a lot of resources. The encryption economy model or Token incentive can encourage individuals and organizations to contribute computing power, data, and other resources by providing Token rewards, thus supporting the construction and operation of the network.
Key Point 5: The Decentralized Finance Model Promotes Efficient Allocation of Resources
By introducing concepts such as stake, lending, and liquidity pools from Decentralized Finance, Edge AI Network is able to establish a marketplace for computing resources. Participants can provide computing power through stakeTokens, lend out excess resources, or contribute to the shared pool in order to receive corresponding rewards. Smart contracts automatically execute these processes, ensuring fair and efficient allocation of resources based on supply and demand, and implementing dynamic pricing mechanisms in the network.
Key Point 6: Trust in Decentralization
In a Decentralization edge device network, establishing trust without central supervision is a challenge. In an encryption network, trust is achieved through mathematical means; this trust based on computation and mathematics is the key to enabling trustless interactions, which AI currently does not possess.
Future Outlook
Looking ahead, there are still a lot of innovative opportunities in the edge AI field. We will see edge AI become an indispensable part of our lives in many application scenarios, such as ultra-personalized learning assistants, digital twins, autonomous driving cars, collective intelligence networks, and emotional AI companions. We are full of expectations for the future!