Notes from Industry

Neural Network Accelerators for Autonomous Vehicles — GPU, FPGA, or ASIC?

Autonomous driving systems are incredibly complex. They tightly integrate multiple state-of-the-art technologies like perception and decision making. Only carefully designed hardware can support these particularly resource-hungry tasks. Furthermore, autonomous driving is one of the first embedded applications that heavily rely on machine-learning algorithms. Thus, massive research effort is put into developing neural network accelerators that meet specific requirements, like redundancy and power efficiency.

Photo by Laura Ockel on Unsplash

The development of autonomous vehicles is undoubtedly one of the most challenging tasks in the current Artificial Intelligence field. An autonomous driving machine has to precisely perceive its environment and plan an adequate set of actions to…

From Computer Vision to Audio Processing through Mel Spectrogram

Spectrograms certainly are the best-suited representation of audio signals for analysis. Not only does it match our understanding of sounds through frequency decomposition, but it also allows us to use 2-dimensional analysis architectures. Given the massive progress in computer vision during the last decade, the possibility of representing sounds as images opens many options. This article will analyze the similitude and limits of the application of computer vision techniques to audio processing. A heavy accent is put on speech recognition and speech synthesis.

“Are we headed towards a future where an AI will be able to out-think us in every way? Then the answer is unequivocally yes.” Elon Musk — Spectrogram generated by the author.

Computer Vision currently is one of the hottest research fields in machine learning. However, audio processing has…

Arthur Findelair

Machine Learning and robotic enthusiast | MSc in Electrical Engineering and Aeronautical Engineering |

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