The essence of this processing method is that there are a bunch of identical computing cores in the GPU, which can process similar but not exactly the same data sets. This allows for more features to be integrated into a single device. Those benefits are that they are very flexible, reusable, and quicker to acquire. GPU has pioneered the creation of application acceleration platforms and complete ecosystems including algorithms such as CNN, DNN, RNN, LSTM, and reinforcement learning networks in the field of deep learning training models. Therefore, the business requirements for data centers are "fast"-the upgrade of the computing power platform must meet the business development as quickly as possible Therefore, the traditional development mode of FPGA is often difficult to meet the needs of the development cycle of half a year or year.Currently, the two most widely used acceleration components in AI computing platforms are GPUs and FPGAs. Feel free to browse our wide range of FPGA Boards to prototype and develop your applications. Attacking is more like parallel processing in embedded applications. They may also think that such a design in an ASIC would require latest geometries and be very expensive.Electric-only vehicles as well as self-charging and plug-in hybrids currently play a modest role in new car sales as the automotive industry slowly develops EV platforms that meet customer expectations as well as the environmental requirements demanded by the...Copyright Swindon Silicon Systems 2020. I call this calculation method "time domain calculation".However, the speed of Internet business iteration is extremely fast, and the accumulation of a large user base may be completed within a few months. In simple terms, the CPU fetches a small amount of data from memory, places it in a register or cache, and then uses a series of instructions to operate on the data. Currently, edge AI is being widely used in the industrial field. Once again this can be planned for right at project start and gives the designer more options.ASICs can often be disregarded when they will give a much better solution. These are areas where FPGAs can take advantage of high throughput.When FPGA becomes a computing power service, with efficient hardware, mature IP and cloud management, what are you still thinking about?Now popular natural language processing and speech recognition are also scenarios where FPGAs can play an advantage.However, if the data sets that need to be processed by instructions are too large, or the data values are too large, the CPU cannot deal with this situation very efficiently.