Recent advancements in hardware and software systems have been driven by the
deployment of emerging smart health and mobility applications. These developments have …

The effectiveness of deep neural networks (DNN) in vision, speech, and language
processing has prompted a tremendous demand for energy-efficient high-performance DNN …

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 …

Purpose This paper aims to review in-memory computing (IMC) for machine learning (ML)
applications from history, architectures and options aspects. In this review, the authors …

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 …

Underscaling the supply voltage (V dd) to ultra-low levels below the safe-operation
threshold voltage (V min) holds promise for substantial power savings in digital CMOS …

In this brief an approach is proposed to achieve energy savings from reduced voltage
operation. The solution detects timing-errors by integrating Algorithm Based Fault Tolerance …