A survey on federated learning for resource-constrained IoT devices
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …
model by learning from multiple decentralized edge clients. FL enables on-device training …
Hardware approximate techniques for deep neural network accelerators: A survey
Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …
A modern primer on processing in memory
Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …
design choice goes directly against at least three key trends in computing that cause …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
DAMOV: A new methodology and benchmark suite for evaluating data movement bottlenecks
Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
A deeper look into rowhammer's sensitivities: Experimental analysis of real dram chips and implications on future attacks and defenses
RowHammer is a circuit-level DRAM vulnerability where repeatedly accessing (ie,
hammering) a DRAM row can cause bit flips in physically nearby rows. The RowHammer …
hammering) a DRAM row can cause bit flips in physically nearby rows. The RowHammer …
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
Figaro: Improving system performance via fine-grained in-dram data relocation and caching
Main memory, composed of DRAM, is a performance bottleneck for many applications, due
to the high DRAM access latency. In-DRAM caches work to mitigate this latency by …
to the high DRAM access latency. In-DRAM caches work to mitigate this latency by …
Dsagen: Synthesizing programmable spatial accelerators
Domain-specific hardware accelerators can provide orders of magnitude speedup and
energy efficiency over general purpose processors. However, they require extensive manual …
energy efficiency over general purpose processors. However, they require extensive manual …
DRAM bender: An extensible and versatile FPGA-based infrastructure to easily test state-of-the-art DRAM chips
To understand and improve DRAM performance, reliability, security, and energy efficiency,
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …