Systems performing scientific computing, data analysis, and machine learning tasks have a
growing demand for application-specific accelerators that can provide high computational …

There has been an arms race for devising accelerators for deep learning in recent years.
However, real-world applications are not only neural networks but often span across …

ML accelerators have largely focused on building general platforms for deep neural
networks (DNNs), but less so on shallow machine learning (SML) algorithms. This paper …

Parameterizable machine learning (ML) accelerators are the product of recent
breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a …

In a typical RTL to GDSII flow, floorplanning or macro placement is a critical step in
achieving decent quality of results (QoR). Moreover, in today's physical synthesis flows (eg …

Edge systems are required to autonomously make real-time decisions based on large
quantities of input data under strict power, performance, area, and other constraints. Meeting …

Over the past decade, innovations in radiation and photonic detectors considerably
improved their resolution, pixel density, sensitivity, and sampling rate, which all contribute to …

Parameterizable ML accelerators are the product of recent breakthroughs in machine
learning (ML). To fully enable the design space exploration, we propose a physical-design …

Deep learning (DL) accelerators are optimized for standard convolution. However,
lightweight convolutional neural networks (CNNs) use depthwise convolution (DwC) in key …

Today's performance analysis frameworks for deep learning accelerators suffer from two
significant limitations. First, although modern convolutional neural network (CNNs) consist of …