Data movement between memory and processors is a major bottleneck in modern
computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this …

Sorting data is needed in many application domains. Traditionally, the data is read from
memory and sent to a general-purpose processor or application-specific hardware for …

Graph convolutional networks (GCNs) have been applied successfully in social networks
and recommendation systems to analyze graph data. Unlike conventional neural networks …

Resistive random-access memory (ReRAM)-based processing-in-memory (PIM)
architectures are used extensively to accelerate inferencing/training with convolutional …

Stochastic Computing (SC) is an alternative computing paradigm that promises high
robustness to noise and outstanding area-and power-efficiency compared to traditional …

Low-discrepancy (LD) bit-streams have been proposed to improve the accuracy and
computation speed of stochastic computing (SC) circuits. These bit-streams are …

Stochastic computing (SC) has emerged as a promising solution for performing complex
functions on large amounts of data to meet future computing demands. However, the …

Stochastic computing (SC) is a re-emerging computing paradigm providing low-cost and
noise-tolerant designs for a wide range of arithmetic operations. SC circuits operate on …

Emerging persistent memory file systems can significantly improve performance by utilizing
the advantages of Persistent Memories (PMs). Especially, they can employ superpages of …

The Internet of Things (IoT) has led to the emergence of big data. Processing this data,
specially in learning algorithms, poses a challenge for current embedded computing …