Publication

Structured pruning for efficient systolic array accelerated cascade Speech-to-Text Translation

2025

Abstract

We present in this paper a simple method for pruning tiles of weights in sparse matrices, that do not require fine-tuning or retraining. This method is applied here to the feed-forward layers of transformers. We assess in a first experiment the impact of such pruning on the performances of speech recognition, machine translation, and the cascaded speech-to-text translation, on the MuST-C database, for the English to French direction. Depending on the size of the pruned tiles (from 4x4 to 32x32), we observe that pruning rates from 15 to 40% for speech recognition and from 40 to 70% for machine translation are feasible for a performance degradation of 10%. Applying this pruning method to the systolic array accelerated version of the cascade speech-to-text translation system results in speedups up to 74x compared to the non-accelerated system. Energy consumption also benefits from structured pruning with a maximum reduction of 35%.

Official source

https://infoscience.epfl.ch/entities/publication/6525c470-b0e2-4bbd-888a-51276776b105

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/6525c470-b0e2-4bbd-888a-51276776b105

About this result

Related concepts (32)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Machine translation

Machine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.

Neural machine translation

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. They require only a fraction of the memory needed by traditional statistical machine translation (SMT) models. Furthermore, unlike conventional translation systems, all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance.

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Structured pruning for efficient systolic array accelerated cascade Speech-to-Text Translation

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