Abstract Design of hardware accelerators for neural network (NN) applications involves
walking a tight rope amidst the constraints of low-power, high accuracy and throughput …

In recent decades, the rate of shrinking integrated-circuit components has slowed as
challenges accumulate. Yet, in part by virtue of an accelerating rate of cleverness, the end …

CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …

Since the Spectre and Meltdown disclosure in 2018, the list of new transient execution
vulnerabilities that abuse the shared nature of microarchitectural resources on CPU cores …

As real-time embedded systems are evolving in scale and complexity, the demand for a
higher performance at a minimum energy consumption has become a necessity …

The use of FPGAs for efficient graph processing has attracted significant interest. Recent
memory subsystem upgrades including the introduction of HBM in FPGAs promise to further …

Random linear network coding (RLNC) has the potential to improve the performance of
current and future Internet of Things (IoT) communication systems, but is computationally …

In recent years, the edge computing paradigm has been attracting much attention in the
Internet-of-Things domain. It aims to push the frontier of computing applications, data, and …

Near Data Processing, in form of processing in-memory (PIM) and in-storage computing
(ISC) was a very active area of research in computer architectures in the'90s [1, 25]. About 3 …

Asymmetric multicore processors (AMP) offer multiple types of cores under the same
programming interface. Extracting the full potential of AMPs requires intelligent scheduling …