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Machine Learning FPGA Applications

01/10/2019, hardwarebee

Machine learning and artificial intelligence have quickly become some of the leading technologies for the present as well as for the near future.

 

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Neural networks on FPGA chips are all the fad right now, and for good reason. A neural network works very similarly to how the human brain works, channeling the ability to observe and modify future behavior patterns based on what was learned from the experience.  This can solve many technological problems such as image recognition, robots, autonomous driving and many more.

 

 

There are several types of neural networks. One of the most commonly used deep learning systems is the Convolutional Neural network or CNN. this type of neural network comprises of a network of neurons that have been artificially designed and executed, using a feedforward information flow to perform functions such as image processing and identification. Learning commences when the flow reverses and a set of weights is produced to calibrate the execution system.

 

Overall, a CNN is based on multiple layers, the number of which is directly related to the performance of the system- the more the number of layers, the better the deep neural network will be at image recognition. These layers include the likes of the convolution layer, which is responsible for extracting low level features from the submitted image like lines and edges, and the pooling layers, which are responsible for reducing the variations of the values in order to ‘pool’ the common features of the image over a particular region for appropriate and accurate recognition. The more the image is run through these layers, the more accurate the identification is.

 

When it comes to implementing CNNs, it is important to ensure that the network is capable of relaying information from one layer to the next when it is put in the pipeline and that it is able to perform the convolutions through the layers efficiently and effectively.

 

FPGAs are most commonly suited for the implementation of CNNs and for good reason at that. FPGA platforms are an amalgamation of computing, logic, and memory capacities, which is why they are looked at as solutions that can help accelerate the CNN training process. It also helps that FPGAs are customizable and reconfigurable chips, well suited towards the streaming nature of the CNN workloads.

 

The reason why FPGAs are the preferred platform for deep neural earning networks is that it has a much greater computational capability when compared to others of its kind. FPGAs are also extremely flexible and can accommodate multiple kinds of data types, including binary, INT8, and FTP32 among many others. This flexibility ensures that FPGAs can actively keep up with the evolving nature of deep and convolutional neural networks, hosting custom data types when and if needed. The architecture of the FPGA is flexible and can be modified and configured whenever needed. This enables the project to continue on smoothly without having to take up unnecessary breaks for updates and architecture modifications.

 

FPGAs are also significantly more powerful and efficient in their function, consuming less power and resources as compared to their counterparts in the likes of GPUs. the architecture of FPGAs comprises of a combination of hardware programmable resources, DPS, and BRAM blocks. These components can be manipulated by the user when configuring in order to maneuver and modify the data path. This means that the user does not have to rely on or be limited by the restrictions imposed by a fixed data path. FPGAs are also able to connect to and extend to any external devices and networks, opening up the possibility of extending the network without having to leverage the power and capacity of another central computation unit.

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