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.
To read the full article, click here
Related Semiconductor IP
- NPU IP Core for Mobile
- NPU IP Core for Edge
- Specialized Video Processing NPU IP
- HYPERBUS™ Memory Controller
- AV1 Video Encoder IP
Related Blogs
- Neoverse CSS N3: Fastest Path to Market Leading Power Efficiency
- A Fast and Seamless Way to Burst to the Cloud for Peak EDA Workloads
- 3 steps to shrinking your code size, your costs, and your power consumption
- Scaling Out Deep Learning (DL) Inference and Training: Addressing Bottlenecks with Storage, Networking with RISC-V CPUs
Latest Blogs
- Cadence Extends Support for Automotive Solutions on Arm Zena Compute Subsystems
- The Role of GPU in AI: Tech Impact & Imagination Technologies
- Time-of-Flight Decoding with Tensilica Vision DSPs - AI's Role in ToF Decoding
- Synopsys Expands Collaboration with Arm to Accelerate the Automotive Industry’s Transformation to Software-Defined Vehicles
- Deep Robotics and Arm Power the Future of Autonomous Mobility