The Expanding Markets for Edge AI Inference
By Geoff Tate, Flex Logix
EETimes (May 27, 2021)
While AI originally was targeted for data centers and in the cloud, it has been moving rapidly towards the edge of the network where it is needed to make fast and critical decisions locally and closer to the end user. Sure, training can be still done in the cloud, but in applications such as autonomous driving, it is important that the time-sensitive decision making (spotting a car or pedestrian) is done closer to the end user (the driver). After all, edge systems can make decisions on images coming in at up to 60 frames per second, enabling quick actions.
These systems are made possible through edge inference accelerators that have emerged to replace CPUs, GPUs and FPGAs at much higher throughput/$ and throughput/Watt.
The ability to do AI inferencing closer to the end user is opening up a whole new world of markets and applications. In fact, IDC just reported that the market for AI software, hardware, and services is expected to break the $500 billion mark by 2024, with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion.
This rapid growth is likely due to the fact that AI is expanding from “just a high-end functionality” into products closer to consumers, essentially bringing AI capabilities to the masses. In addition, recent products announced have started breaking the cost barriers typically associated with AI inference, enabling designers to incorporate AI into a wider range of affordable products.
To read the full article, click here
Related Semiconductor IP
- AI inference engine for real-time edge intelligence
- AI inference engine for Audio
- Neural engine IP - AI Inference for the Highest Performing Systems
- Neural engine IP - Balanced Performance for AI Inference
- AI inference processor IP
Related Articles
- Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure
- Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
- AI Edge Inference is Totally Different to Data Center
- MIPI in next generation of AI IoT devices at the edge
Latest Articles
- A Low-Latency ASIC Architecture for Real-Time Line Segment Detection
- BitFair: A 12nm Bit-Serial CNN Accelerator with Learnable Early Termination and Adaptive Bit Ordering for Ultra-Low-Power XR Vision
- A Flexible Sparsity-Aware FPGA Accelerator with Column-Wise Compression for Efficient CNN Inference
- Reducing Instruction-Fetch Energy in RISC-V for Embedded AI Processing via Dynamic and Static Loop Caching
- SPARC: Automated Root-Cause Analysis of Pre-Silicon Power Side-Channel Leakage in the Processor Design Flow