How FGPA Accelerates Deep Learning Network Implementation

How FGPA Accelerates Deep Learning Network Implementation

The unique flexibility of the FPGA fabric is accelerating the implementation of deep learning networks.   

FREMONT, CA: Recent advances in digital technologies and the availability of credible data have led to the emergence of an era of artificial intelligence, deep learning network, with its ability and effectiveness in solving complex learning problems that is not possible before. Hardware accelerators have been employed to improve the throughput of deep learning networks. Among several accelerators, FPGAs have been widely adopted for speeding up the implementation of deep learning networks because of their ability to maximize parallelism and energy efficiency. 

FPGAs enable a flexible and customizable architecture, which enables the usage of only the computing resources that users need. Having low-power systems for deep learning networks is critical in many applications, such as ADAS. FPGAs, considered raw programmable hardware, makes them easy to use and reduces the time to market significantly. To catch up with daily-evolving machine learning algorithms, having the capability to reprogram the system is exceptionally beneficial rather than waiting for long fabrication time.

Block RAM in the FPGA offers 50 times faster data transfer compared to the fastest off-chip memories. This feature is a game-changer for deep learning applications, for which low latency is essential. FPGAs are configurable raw hardware, and there is no fixed architecture or data path to tie down. This flexibility enables FPGAs to do massively parallel processing since the data path could be reconfigured at any time. The flexibility also brings any-to-any connection capability enabling FPGAs to connect to any device, network, or storage devices without the need for a host CPU.

FPGA users can implement any safety features in the hardware. Encoding can be done with high efficiency, depending on the application. FPGA are used in avionics, automation, and security, which are proof of its functional safety in these devices that deep learning algorithms could benefit from it. FPGAs are reconfigurable, and it can reduce the time to market for an application is low.

Surely, FPGAs have to play an increasingly important role in data sampling and processing industries due to its highly parallel architecture, low power consumption, and flexibility in custom algorithms. In the artificial intelligence field, FGPA will be vital for training and implementing deep learning algorithms.