SEIFER: Scalable Edge Inference for Deep Neural Networks

This repository includes code for both:

1. A. Parthasarathy and B. Krishnamachari, “SEIFER: Scalable Edge Inference for Deep Neural Networks,” presented at the Challenges in Deploying and Monitoring Machine Learning Systems (DMML) Workshop as part of the Conference on Neural Information Processing Systems (NeurIPS), 2022.

• This paper can be found here: https://arxiv.org/abs/2210.12218

3. A. Parthasarathy and B. Krishnamachari, “Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughput,” presented at the International Telecommunication Networks and Applications Conference (ITNAC), 2022.

• This paper can be found here: https://arxiv.org/abs/2210.12219