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
- 12-bit, 400 MSPS SAR ADC - TSMC 12nm FFC
- 10-bit Pipeline ADC - Tower 180 nm
- NoC Verification IP
- Simulation VIP for Ethernet UEC
- Automotive Grade PLLs, Oscillators, SerDes PMAs, LVDS/CML IP
Related Blogs
- Powering Up Efficiency: A Deep Dive into CXL L0p and its Verification
- Neoverse CSS N3: Fastest Path to Market Leading Power Efficiency
- 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
- CAVP-Validated Post-Quantum Cryptography
- The role of AI processor architecture in power consumption efficiency
- Evaluating the Side Channel Security of Post-Quantum Hardware IP
- A Golden Source As The Single Source Of Truth In HSI
- ONFI 5.2: What’s new in Open NAND Flash Interface's latest 5.2 standard