Microsoft

PipeThreader: Software-Defined Pipelining for Efficient DNN Execution

To effectively utilize heterogeneous specialized hardware units in modern GPUs, such as TensorCores and Tensor Memory Accelerators, this paper introduces PipeThreader, a new DNN compiler. PipeThreader ...
Microsoft

Uncovering Nested Data Parallelism and Data Reuse in DNN Computation with FractalTensor

To speed up computation, deep neural networks (DNNs) usually rely on highly optimized tensor operators. Despite the effectiveness, tensor operators are often defined empirically with ad hoc semantics.
GitHub

Control AIRMX Pro with JavaScript

The package utilizes the MQTT protocol to communicate with your AIRMX devices. Once connected to the server, it constantly monitors the status updates from your machines. Additionally, it provides a ...
IEEE

Joint Task Offloading, DNN Pruning, and Computing Resource Allocation for Fault Detection With Dynamic Constraints in Industrial IoT

Abstract: In this paper, we investigate a joint task offloading, deep neural network (DNN) model pruning, and edge computing resource allocation (JOPA) problem for supporting a fault detection service ...
IEEE

Dual-Mode Rounding Algorithms and Hardware for Posit-based DNN Training: The Future of Mixed Precision Frameworks

Abstract: This paper presents a Posit-based Mixed Precision (PMP) framework for deep neural network (DNN) training and inference, leveraging Posit32, Posit16, and Posit8 across different computational ...