In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …

Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …

The “memory wall” problem or so-called von Neumann bottleneck limits the efficiency of
conventional computer architectures, which move data from memory to CPU for …

Processing-using-DRAM has been proposed for a limited set of basic operations (ie, logic
operations, addition). However, in order to enable full adoption of processing-using-DRAM …

Genome sequence analysis has enabled significant advancements in medical and scientific
areas such as personalized medicine, outbreak tracing, and the understanding of evolution …

Many modern and emerging applications must process increasingly large volumes of data.
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …

Emerging edge computing platforms often contain machine learning (ML) accelerators that
can accelerate inference for a wide range of neural network (NN) models. These models are …

Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …