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20212026
most citedHardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

3 citations · 4 across the 4 of their papers we have counts for

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cs.CL2022★ 1 cited

Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization

Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano +5

We report on aggressive quantization strategies that greatly accelerate inference of Recurrent Neural Network Transducers (RNN-T). We use a 4 bit integer representation for both we…

cs.CL2021

4-bit Quantization of LSTM-based Speech Recognition Models

Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano +9

We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition…