RowHammer (RH) is a significant and worsening security, safety, and
reli...
Prior works propose SRAM-based TRNGs that extract entropy from SRAM arra...
Resistive Random-Access Memory (RRAM) is well-suited to accelerate neura...
DRAM-based main memory is used in nearly all computing systems as a majo...
Processing-using-memory (PuM) techniques leverage the analog operation o...
On-chip memory (usually based on Static RAMs-SRAMs) are crucial componen...
Energy-efficiency is a key concern for neural network applications. To
a...
Modern computing devices employ High-Bandwidth Memory (HBM) to meet thei...
Modern large-scale computing systems (data centers, supercomputers, clou...
In this paper, we exploit the aggressive supply voltage underscaling
tec...
We empirically evaluate an undervolting technique, i.e., underscaling th...
Deep Neural Networks (DNNs) are inherently computation-intensive and als...
This paper presents a deeply pipelined and massively parallel Binary Sea...
To improve power efficiency, researchers are experimenting with dynamica...
Voltage underscaling below the nominal level is an effective solution fo...
Machine Learning (ML) is making a strong resurgence in tune with the mas...