Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

The explosive growth in data-centric artificial intelligence related applications necessitates a
radical departure from traditional von Neumann computing systems, which involve separate …

Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical
way, by enabling machine learning in the industry, business, health, transportation, and …

Field Programmable Gate Arrays (FPGAs) are spatial architectures with a heterogeneous
reconfigurable fabric. They are state-of-the-art for prototy**, telecommunications …

We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting
sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators …

Ultra-low-power sensor nodes enable many new applications and are becoming
increasingly pervasive and important. Energy efficiency is the key determinant of the value of …

We study and analyze the fundamental aspects of noise propagation in recurrent as well as
deep, multilayer networks. The motivation of our study is neural networks in analog …

A Coarse-Grained Reconfigurable Array (CGRA) is a promising high-performance low-
power accelerator for compute-intensive loop kernels. While the map** of the …

Edge AI accelerators have been emerging as a solution for near customers' applications in
areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable …

This position paper examines a spectrum of approaches to overcoming the limited
computing power of mobile devices caused by their need to be small, lightweight and …