Propelled into existence riding on low NRE (Non-recurring Engineering) cost, the FPGAs began as an alternative to ASIC until the customer required beyond a number of units, which was called a cross-over point at which higher NRE cost of ASIC was justifiable. Slowly the flexibility of programmable logic helped FPGA vendors create a room for their product. The IEEE article mentions that 2/3rd of the designs were losing money at one point because of changing requirements, product failures, or outright design errors.
Today, FPGAs are forming the backbone for 5G, Embedded Vision, Smart World (Cities, Factories, and so on), Cloud platforms, and safety-critical systems. FPGAs are finding application in various sectors which have been very specific about performance and compliance requirements such as defense, aerospace, and automotive.
The differentiator for FPGAs has been the flexibility and the operating range. On the one hand, FPGAs can power the high-performance cloud data-centers requiring as much as several hundred watts, and on the other hand, powering feature-light apps running low-power designs drawing 1/1000th of a watt (1mW). Thus, FPGAs can accelerate searches for Bing search engine on Microsoft at lower power consumption. And the same assembly could also be hosting specialized low-power FPGAs to run specific operations such as controlling the system, securing firmware, etc.
Machine Learning and Artificial adoption in our world is going to boost the demand for FPGAs, considering these fields are still evolving. They need flexible programmability to support agile development cycles of end use-cases. There are so many cases where low-power FPGA designs can support object detection, counting operations, key phrase detection to enable complex use-cases, which make a more durable case for mass-adoption than ever foreseeable. However, it requires FPGAs to support stringent low-power demands at much smaller form factor.
Lattice Semiconductor provides specialized low-power FPGA offering to perform computer vision and AI inference applications. Adopting a platform-based approach to product development resulting in maximum design-reuse, Lattice Semiconductor has launched platforms more frequently at a considerably lower cost. Lattice Nexus and Lattice Crosslink-NX platforms are likely to extend the low-power advantage that earlier FPGAs have enjoyed for a long time.
The Edge computing devices, a backbone of ML/AI advancements, have been constrained by battery juice and connectivity speed for fast and frequent data transmission. The AI has seen limited adoption mainly because of inadequate ability to process large datasets caused by the low transmission per set timeframe. However, with 5G adoption around the corner, this limitation will become a thing of the past. Now, with both problems, low-power and transmission speed, being addressed, these FPGA based solutions are all set to reach mass-production as more connected devices proliferate riding on the widespread 5G network.
The low-power FPGAs providers are going to see consistently higher demand for FPGA units. However, they are going to be continuously challenged to serve the incoming custom-design requirements considering large deviations in end-use. That’s where the boutique design houses can offload the FPGA companies and become the enablers for the mass-adoption of AI applications running on FPGAs. Someone who can handle RTL complexities, build required ML firmware, edit drivers, and get the system working for the desired ML use case.
Know how Softnautics can help you design FPGA-Powered ML solution for your use-case.