Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

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

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 …

Computing systems are widely used in medical diagnosis, climate prediction, autonomous
vehicles, etc. As the key part of electronics, the performance of computing systems is crucial …

We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …

Transformer-based models are state-of-the-art for many machine learning (ML) tasks.
Executing Transformer usually requires a long execution time due to the large memory …

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

Many modern workloads such as neural network inference and graph processing are
fundamentally memory-bound. For such workloads, data movement between memory and …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Commodity DRAM-based processing-using-memory (PuM) techniques that are supported
by off-the-shelf DRAM chips present an opportunity for alleviating the data movement …