When you purchase through links on our site, we may earn an affiliate commission.Heres how it works.
The concept of AI accelerators has evolved over the years.
In the early days, AI tasks were usually handed over to conventional CPUs.
While they work, conventional CPUs arent really adept at handling the intensive computational demands of AI workloads.
The most popular AI accelerators includeGoogleTPU v5p,NvidiaA100 and H100,AMDInstinct MI300X, andIntelGaudi 3.
GPUsare perhaps the most widely used jot down of AI accelerator.
Thanks to their parallel processing capabilities, they are a popular choice for training AI models.
However, they can be power-hungry and arent the best choice for very large-scale applications.
Field programmable gate arrays (FPGAs) are another popular pop in of AI accelerator.
Although more expensive than GPUs, FPGAs are often used for real-time AI applications such as autonomous vehicles.
utility-specific integrated circuits (ASICs) are purpose-built chips designed with a specific purpose or workload in mind.
ASICs are typically used for large-scale AI applications such as deep learning.
Neural Processing Units (NPUs)are AI accelerators optimized for use in neural networks, and deep learning.
Unlike CPUs and GPUs, these AI chips are purpose-built for AI applications.
This makes them essential for AI applications such as autonomous vehicles, healthcare, and finance.
What makes AI accelerators special?
As weve said, AI accelerators are specialized hardware devices that help facilitate the execution of AI workloads efficiently.
They deliver superior performance as compared to traditional computing chips, and they do this while minimizing energy consumption.
This helps maximize throughput and reduces processing times by an order of magnitude.
This not only allows these chips to handle large datasets efficiently but also cuts down memory access latency.
This hierarchy helps facilitate rapid data access and also minimizes latency during complex AI computations.
Software Optimization:To fully leverage their capabilities, AI accelerators require software optimization.
This includes everything from library optimizations for specific AI frameworks, to runtime optimizations.
