Bringing Power Efficiency to TinyML, ML-DSP and Deep Learning Workloads
In recent times, the need for real-time decision making, reduced data throughput, and privacy concerns, has moved a substantial portion of AI processing to the edge. This shift has given rise to a multitude of Edge AI applications, each introducing its unique set of requirements and challenges. And a $50B AI SoC market is forecast for 2025 [Source: Pitchbook Emerging Tech Research], with Edge AI chips expected to make up a significant portion of this market.
The Shift of AI processing to the edge and its Power Efficiency Imperative
The shift of AI processing to the edge marks a new era of real-time decision-making across a range of applications, from IoT sensors to autonomous systems. This shift helps reduce latency which is critical for instant responses, enhances data privacy through local processing, enables offline functionality, and ensures uninterrupted operation in remote or challenging environments. As these edge applications run under energy constrained conditions and battery powered devices, power efficiency takes center stage in this transformative landscape.
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