17 citations · 99 across the 66 of their papers we have counts for
4 papers · 1 filter
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch
Souvik Kundu, Shikai Wang, Qirui Sun +2
Large DNNs with mixed-precision quantization can achieve ultra-high compression while retaining high classification performance. However, because of the challenges in finding an ac…
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic
Mahdi Nazemi, Arash Fayyazi, Amirhossein Esmaili +3
While there is a large body of research on efficient processing of deep neural networks (DNNs), ultra-low-latency realization of these models for applications with stringent, sub-m…
BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification
Seyed Abolfazl Ghasemzadeh, Erfan Bank Tavakoli, Mehdi Kamal +2
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…
A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks
Mohsen Ahmadzadeh, Mehdi Kamal, Ali Afzali-Kusha +1
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. By exploiting a…