We present Polygeist, a new compilation flow that connects the MLIR compiler infrastructure
to cutting edge polyhedral optimization tools. It consists of a C and C++ frontend capable of …

Because of the increasing demand for intensive computation in deep neural networks,
researchers have developed both hardware and software mechanisms to reduce the …

In recent years, we are witnessing a trend toward in-memory computing for future
generations of computers that differs from traditional von-Neumann architecture in which …

Memristive devices promise an alternative approach toward non-Von Neumann
architectures, where specific computational tasks are performed within the memory devices …

This paper explores the challenges and opportunities of integrating non-volatile memories
(NVMs) into embedded systems for machine learning. NVMs offer advantages such as …

Computing-in-Memory (CIM) architectures using Non-volatile Memories (NVMs) have
emerged as a promising way to address the “memory wall” problem in traditional Von …

In recent years, various computing-in-memory (CIM) processors have been presented,
showing superior performance over traditional architectures. To unleash the potential of …

One of the main challenges for modern processors is the data transfer between processor
and memory. Such data movement implies high latency and high energy consumption. In …

Computation-in-memory (CIM) shows great promise for specific applications by employing
emerging (non-volatile) memory technologies such as memristors for both storage and …

Memristor-based processing-in-memory architecture is a promising solution to the memory
bottleneck in the neural network (NN) processing. A major challenge for the programmability …