A comprehensive review of model compression techniques in machine learning
PV Dantas, W Sabino da Silva Jr, LC Cordeiro… - Applied …, 2024 - Springer
This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …
Comprehending in-memory computing trends via proper benchmarking
NR Shanbhag, SK Roy - 2022 IEEE Custom Integrated Circuits …, 2022 - ieeexplore.ieee.org
Since its inception in 2014 [1], the modern version of in-memory computing (IMC) has
become an active area of research in integrated circuit design globally for realizing artificial …
become an active area of research in integrated circuit design globally for realizing artificial …
Benchmarking in-memory computing architectures
NR Shanbhag, SK Roy - IEEE Open Journal of the Solid-State …, 2022 - ieeexplore.ieee.org
In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …
implement energy-efficient machine learning (ML) systems. However, today, the energy …
[PDF][PDF] A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance
Y Zhang, RC Muniyandi, F Qamar - 2024 - preprints.org
In the rapid development of the Internet of Things (IoT) and large-scale distributed networks,
Intrusion Detection Systems (IDS) face significant challenges in handling complex …
Intrusion Detection Systems (IDS) face significant challenges in handling complex …