Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies
on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple …

Nearest neighbor (NN) search computations are at the core of many applications such as
few-shot learning, classification, and hyperdimensional computing. As such, efficient …

Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive
applications, and SRAM technology scaling and leakage power limits the efficiency of …

Hf0. 5Zr0. 5O2 (HZO)-based ferroelectric random access memory (FeRAM) is a good
candidate for the embedded nonvolatile memory (eNVM) applications because of its high …

Transformer networks have outperformed recurrent and convolutional neural networks in
terms of accuracy in various sequential tasks. However, memory and compute bottlenecks …

The rapidly increasing volume and complexity of data is demanding the relentless scaling of
computing power. With transistor feature size approaching physical limits, the benefits that …

On-device learning is essential to achieve intelligence at the edge, where it is desirable to
learn from few samples or even just a single sample. Memory-augmented neural networks …

Multiple research vectors represent possible paths to improved energy and performance
metrics at the application-level. There are active efforts with respect to emerging logic …

Data-intensive applications do not fully utilize the compute capabilities of Von Neumann
architectures because of the memory bandwidth bottleneck. These memory-bandwidth …

Data movement and processing have become significant bottlenecks for conventional Von
Neumann architectures, especially as dataset sizes continue to grow. This issue is …