Hybrid computing platforms, comprising CPU cores and FPGA logic, are increasingly used
for accelerating data-intensive workloads in cloud deployments, and are a growing topic of …

Machine Learning (ML) is making a strong resurgence in tune with the massive generation
of unstructured data which in turn requires massive computational resources. Due to the …

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Modern large-scale computing systems (data centers, supercomputers, cloud and edge
setups and high-end cyber-physical systems) employ heterogeneous architectures that …

Modern computing devices employ High-Bandwidth Memory (HBM) to meet their memory
bandwidth requirements. An HBM-enabled device consists of multiple DRAM layers stacked …

Modern large-scale computing systems (data centers, supercomputers, cloud and edge
setups and high-end cyber-physical systems) employ heterogeneous architectures that …

A major concern with battery-operated devices is power-awareness and its appropriate
computing. The power dissipation of such systems is usually considered a hardware …

On-chip memory (usually based on Static RAMs—SRAMs) are crucial components for
various computing devices including heterogeneous devices, eg, GPUs, FPGAs, and ASICs …

The use of GPUs to accelerate DNN training and inference is already widely adopted,
allowing for a significant performance increase. However, this performance is usually …

Voltage underscaling below the nominal level is an effective solution for improving energy
efficiency in digital circuits, eg, Field Programmable Gate Arrays (FPGAs). However, further …