The demand to process vast amounts of data generated from state-of-the-art high resolution
cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such …

Unprecedented penetration of artificial intelligence (AI) algorithms has brought about rapid
innovations in electronic hardware, including new memory devices. Nonvolatile memory …

High-quality 3D image recognition is an important component of many vision and robotics
systems. However, the accurate processing of these images requires the use of compute …

Since its inception in 2014 [1], the modern version of in-memory computing (IMC) has
become an active area of research in integrated circuit design globally for realizing artificial …

In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …

Due to the high activation sparsity and use of accumulates (AC) instead of expensive
multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have …

This paper presents signal processing methods to enhance the energy vs. accuracy trade-off
of in-memory computing (IMC) architectures. First, an optimal clip** criterion (OCC) for …

Charge-domain SRAM-based Computing-in-memory (CIM) proves to be a promising
method for DNN inference, and benefits from avoiding data movement between computing …

Hyperspectral cameras generate a large amount of data due to the presence of hundreds of
spectral bands as opposed to only three channels (red, green, and blue) in traditional …

Analog Computing-in-Memory (ACIM) is an emerging architecture to perform efficient AI
edge computing. However, current ACIM designs usually have unscalable topology and still …