5 citations · 7 across the 6 of their papers we have counts for
6 papers
LL-ViT: Edge Deployable Vision Transformers with Look Up Table Neurons
Shashank Nag, Alan T. L. Bacellar, Zachary Susskind +9
Vision Transformers have been tremendously successful in computer vision tasks. However, their large computational, memory, and energy demands are a challenge for edge inference on…
Shrinking the Giant : Quasi-Weightless Transformers for Low Energy Inference
Shashank Nag, Alan T. L. Bacellar, Zachary Susskind +9
Transformers are set to become ubiquitous with applications ranging from chatbots and educational assistants to visual recognition and remote sensing. However, their increasing com…
Differentiable Weightless Neural Networks
Alan T. L. Bacellar, Zachary Susskind, Mauricio Breternitz +4
We introduce the Differentiable Weightless Neural Network (DWN), a model based on interconnected lookup tables. Training of DWNs is enabled by a novel Extended Finite Difference te…
ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks
Zachary Susskind, Aman Arora, Igor D. S. Miranda +9
The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches t…
Weightless Neural Networks for Efficient Edge Inference
Zachary Susskind, Aman Arora, Igor Dantas Dos Santos Miranda +8
Weightless Neural Networks (WNNs) are a class of machine learning model which use table lookups to perform inference. This is in contrast with Deep Neural Networks (DNNs), which us…
Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization
Zachary Susskind, Bryce Arden, Lizy K. John +2
Neuro-symbolic artificial intelligence is a novel area of AI research which seeks to combine traditional rules-based AI approaches with modern deep learning techniques. Neuro-symbo…