136 citations · 312 across the 11 of their papers we have counts for
11 papers
Power-Performance Characterization of TinyML Systems
Yujie Zhang, Dhananjaya Wijerathne, Zhaoying Li +1
TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper presents a systematic performanc…
Accelerating Unstructured SpGEMM using Structured In-situ Computing
Huize Li, Tulika Mitra
Sparse matrix-matrix multiplication (SpGEMM) is a critical kernel widely employed in machine learning and graph algorithms. However, real-world matrices' high sparsity makes SpGEMM…
Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham +2
Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Ex…
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data
Kuluhan Binici, Nam Trung Pham, Tulika Mitra +1
With the increasing popularity of deep learning on edge devices, compressing large neural networks to meet the hardware requirements of resource-constrained devices became a signif…
IsoRAN: Isolation and Scaling for 5G RANvia User-Level Data Plane Virtualization
Nishant Budhdev, Mun Choon Chan, Tulika Mitra
5G presents a unique set of challenges for cellular network architecture. The architecture needs to be versatile in order to handle a variety of use cases. While network slicing ha…
Simultaneous Progressing Switching Protocols for Timing Predictable Real-Time Network-on-Chips
Niklas Ueter, Georg von der Brueggen, Jian-Jia Chen +2
Many-core systems require inter-core communication, and network-on-chips (NoCs) have been demonstrated to provide good scalability. However, not only the distributed structure but…