66 citations · 191 across the 14 of their papers we have counts for
3 papers · 1 filter
Ternary Residual Networks
Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi +4
Sub-8-bit representation of DNNs incur some discernible loss of accuracy despite rigorous (re)training at low-precision. Such loss of accuracy essentially makes them equivalent to…
Ternary Neural Networks with Fine-Grained Quantization
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere +3
We propose a novel fine-grained quantization (FGQ) method to ternarize pre-trained full precision models, while also constraining activations to 8 and 4-bits. Using this method, we…
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
Naveen Mellempudi, Abhisek Kundu, Dipankar Das +2
We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy. In addition, we also constrai…