When would you use an FPGA ?
Maybe you need to optimize a chip for a particular workload, or you need to make changes at the chip level for some sort of upgrade later on. Use cases for FPGAs cover a wide range of areas and verticals.
They could be used inside equipment for video and imaging or advanced circuitry for a computer, or maybe inside a smarter Tesla , think Boeing or Airbus planes utilization, or even intelligent autonomous military drones or weaponry. The use cases and possibilities might be endless. Or infinite.
FPGAs are useful for prototyping or piloting application-specific integrated circuits (ASICs) or processors. Bitcoin mining for example uses the concept of ASICS.
These devices and boards can be reprogrammed until the ASIC or processor design is final and bug-free and the actual manufacturing of the final ASIC begins.
The company Intel sometimes uses FPGAs to prototype new chips.
They recently purchased or bought a company called eASIC.
Why you might ask ?
Intel’s thought process might be a way to accelerate its designing and prototyping process. This company , eASIC, produced something called a “structured ASIC,” which relies on a model that is in between an ASIC and an FPGA.
This ASIC with a FPGA design philosophy bakes the fixed layout into a single design mask for manufacturing. Far more efficient and optimized design process for prototyping. By being a fixed design like an ASIC, it is faster than a variable design, but without the die area benefits of ASIC-like power savings. However, it was designed in FPGA time, rather than ASIC time (up to six months saved), and saves power through its fixed design.
So what can a enterprise business or small business user do with an FPGA ?
FPGAs can be useful to SMBs and enterprise businesses because they can be dynamically reprogrammed with a data path that exactly matches a specific workload.
Think in terms of business and technology processes like Data processing , advanced data analytics, image recognition, data and network encryption, and data compression. Optimized FPGAs are also more power-efficient than running equivalent workloads on a CPU. So a great use case for the Internet of things (IOT). Far better total cost of ownership (TCO) and versatility too.
FPGAs are starting to become important in IOT and fields like Artificial intelligence, machine learning, AI on the edge, and neural networks
More importantly, FPGAs are gaining prominence in deep neural networks (DNNs).
Running DNN inference models takes significant processing power. Think of that P3 AWS bill. Graphics processing units (GPUs) are often used to accelerate inference processing, but in some cases, high-performance FPGAs might actually outperform GPUs in analyzing large amounts of data for machine learning.
AWS has FPGA ec2 instances available now
These Amazon AWS EC2 F1 instances use FPGAs to enable delivery of custom hardware accelerations. F1 instances are easy to program and come with everything you need to develop, simulate, debug, and compile your hardware acceleration code, including an FPGA Developer AMI and supporting hardware level development on the cloud.
Using these F1 instances to deploy hardware accelerations can be useful in many applications to solve complex science, engineering, and business problems that require high bandwidth, enhanced networking, and very high compute capabilities.
Think of use cases where you might have a modest number of distinct operations that account for significant portions of application in run-time. These could be very useful for big data analytics, genomics, electronic design automation (EDA), image and video processing, compression, security, and search/analytics.
Microsoft is also starting to put Intel FPGA versatility to use on their Azure cloud platform
Microsoft’s Project Brainwave provides customers with access to Intel Stratix FPGAs through Microsoft Azure cloud services. The cloud servers outfitted with these FPGAs have been configured specifically for running deep learning models. The Microsoft service lets developers harness the power of FPGA chips without purchasing and configuring specialized hardware and software. Instead, developers can work with common open-source tools, such as the Microsoft Cognitive Toolkit or TensorFlow AI development framework.
FPGAs are becoming very important. And useful.
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