NAND-based solid-state disks (SSDs) are known for their superior random read/write
performance due to the high degrees of multi-chip parallelism they exhibit. Currently, as the …

Emerging computational resistive memory is promising to overcome the challenges of
scalability and energy efficiency that DRAM faces and also break through the memory wall …

Billions of Internet-of-Things (IoT) devices such as sensors, actuators, computing units, etc.,
are connected to form IoT platforms. However, it is observed that such hardware platforms …

Strided memory accesses are an important type of operations for In-Memory Databases
(IMDB) applications. Strided memory accesses often demand data at word granularity with …

Nonvolatile memory (NVM)-based training-in-memory (TIME) systems have emerged that
can process the neural network (NN) training in an energy-efficient manner. However, the …

Making embedded memory symmetric provides the capability of memory access in both
rows and columns, which brings new opportunities of significant energy and time savings if …

Changes in application trends along with increasing number of connected devices have led
to explosion in the amount of data being generated every single day. Computing systems …

Our work proposes a new Ferroelectric FET (FeFET) based Ternary Content Addressable
Memory (TCAM) with features of integrated search and read operations (along with write) …

Convolutional neural networks (CNNs) are one of the most popular machine learning
algorithms. The convolutional layers, which account for the most execution time of CNNs …

Previous works have shown the possibility of constructing row-column and multi-stride
memory systems that can exploit simultaneously dense access along multiple logical data …