The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Recent studies on software tool manipulation with large language models (LLMs) mostly rely
on closed model APIs. The industrial adoption of these models is substantially constrained …

Deep Neural Networks (DNNs) have emerged as the core enabler of many major
applications on mobile devices. To achieve high accuracy, DNN models have become …

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal
dynamics are getting wide attention and are being applied to many relevant problems using …

High-level synthesis (HLS) has been widely adopted as it significantly improves the
hardware design productivity and enables efficient design space exploration (DSE). Existing …

Transformers are one of the most important machine learning workloads today. Training one
is a very compute-intensive task, often taking days or weeks, and significant attention has …

Elucidating the intricate neural mechanisms underlying brain functions requires integrative
brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose …

Training machine learning models requires feeding input data for models to ingest. Input
pipelines for machine learning jobs are often challenging to implement efficiently as they …