FVLLMONTI: The 3D Neural Network Compute Cube $(N^{2}C^{2})$ Concept for Efficient Transformer Architectures Towards Speech-to-Speech Translation
Résumé
This multi-partner-project contribution introduces the midway results of the Horizon 2020 FVLLMONTI project. In this project we develop a new and ultra-efficient class of ANN accelerators, the neural network compute cube (N2C2 ), which is specifically designed to execute complex machine learning tasks in a 3D technology, in order to provide the high computing power and ultra-high efficiency needed for future edge-AI applications. We showcase its effectiveness by targeting the challenging class of Transformer ANNs, tailored for Automatic Speech Recognition and Machine Translation, the two fundamental components of speech-to-speech translation. To gain the full benefit of the accelerator design, we develop disruptive vertical transistor technologies and execute design-technology-co-optimization (DTCO) loops from single device, to cell and compute cube level. Further, a hardware-software-co-optimization is executed, e.g. by compressing the executed speech recognition and translation models for energy efficient executing without substantial loss in precision.
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