27 citations · 41 across the 6 of their papers we have counts for
6 papers
Restructurable Activation Networks
Kartikeya Bhardwaj, James Ward, Caleb Tung +6
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called…
Armour: Generalizable Compact Self-Attention for Vision Transformers
Lingchuan Meng
Attention-based transformer networks have demonstrated promising potential as their applications extend from natural language processing to vision. However, despite the recent impr…
Collapsible Linear Blocks for Super-Efficient Super Resolution
Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil +6
With the advent of smart devices that support 4K and 8K resolution, Single Image Super Resolution (SISR) has become an important computer vision problem. However, most super resolu…
Efficient Winograd Convolution via Integer Arithmetic
Lingchuan Meng, John Brothers
Convolution is the core operation for many deep neural networks. The Winograd convolution algorithms have been shown to accelerate the widely-used small convolution sizes. Quantize…
Automatic Library Generation for Modular Polynomial Multiplication
Lingchuan Meng
Polynomial multiplication is a key algorithm underlying computer algebra systems (CAS) and its efficient implementation is crucial for the performance of CAS. In this paper we desi…
Automatic Generation of Vectorized Montgomery Algorithm
Lingchuan Meng
Modular arithmetic is widely used in crytography and symbolic computation. This paper presents a vectorized Montgomery algorithm for modular multiplication, the key to fast modular…