A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks

no code implementations • 24 Jan 2021 • Mohsen Ahmadzadeh, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called A2P-MANN.

Question Answering

BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification

no code implementations • 7 Jan 2021 • Seyed Abolfazl Ghasemzadeh, Erfan Bank Tavakoli, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

In this paper, first, a hardware-friendly pruning algorithm for reducing energy consumption and improving the speed of Long Short-Term Memory (LSTM) neural network accelerators is presented.

Sentiment Analysis Sentiment Classification +2

Space Expansion of Feature Selection for Designing more Accurate Error Predictors

no code implementations • 30 Dec 2018 • Shayan Tabatabaei Nikkhah, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

The results on various benchmarks demonstrate significant improvements in the prediction accuracy compared to the prior works which used only the accelerator inputs for the prediction.

feature selection Scheduling