Arizona State University

Fengbo Ren

Zhikang Zhang

Kai Xu

Michael F Riera

Erfan Bank Tavakoli

Masudul Hassan Quraishi

Yixing Li

Akshay Dua

Compute-centric message passing interface (CMPI) provides a hardware-agnostic message passing interface for heterogeneous computing systems. Hardware-agnostic programming with high performance portability is envisioned to be a bedrock for realizing adoption of emerging accelerator technologies in heterogeneous computing systems, such as high-performance computing (HPC) systems, data center computing systems, and edge computing systems. The adoption of emerging accelerators is key to achieving greater scale and performance in heterogeneous computing systems. Accordingly, embodiments described herein provide a flexible hardware-agnostic environment that allows application developers to develop high-performance applications without knowledge of the underlying hardware.

C2MPI: a hardware-agnostic message passing interface for heterogeneous computing systems

An OpenCL-defined scalable runtime-flexible programmable accelerator architecture for accelerating convolutional neural network (CNN) inference in cloud/edge computing is provided, referred to herein as Systolic-CNN. Existing OpenCL-defined programmable accelerators (e.g., field-programmable gate array (FPGA)-based accelerators) for CNN inference are insufficient due to limited flexibility for supporting multiple CNN models at runtime and poor scalability resulting in underutilized accelerator resources and limited computational parallelism. Systolic-CNN adopts a highly pipelined and paralleled one-dimensional (1-D) systolic array architecture, which efficiently explores both spatial and temporal parallelism for accelerating CNN inference on programmable accelerators (e.g., FPGAs). Systolic-CNN is highly scalable and parameterized, and can be easily adapted by users to efficiently utilize the coarse-grained computation resources for a given programmable accelerator. In addition, Systolic-CNN is runtime-flexible and can be time-shared to accelerate a variety of CNN models at runtime without the need to recompile the programmable accelerator kernel hardware or reprogram the programmable accelerator.

Systolic-CNN: an OpenCL-defined scalable runtime-flexible programmable accelerator architecture for accelerating convolutional neural network inference in cloud/edge computing

Various embodiments of a system and method for pruning binary neural networks by analyzing weight flipping frequency and pruning the binary neural network based on the weight flipping frequency associated with each channel of the binary neural network are disclosed herein.

Systems and methods for pruning binary neural networks guided by weight flipping frequency

This disclosure addresses the single-image compressive sensing (CS) and reconstruction problem. A scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) facilitates high-fidelity, flexible and fast CS image reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian pyramid through multiple stages of reconstructive adversarial networks (RANs). At each pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods.

Lapran: a scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction

A compression ratio (CR) adapter (CRA) for end-to-end data-driven compressive sensing (CS) reconstruction (EDCSR) frameworks is provided. EDCSR frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy for images and other signals. However, existing EDCSR frameworks cannot adapt to a variable CR. For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. Embodiments described herein present a CRA framework that addresses the variable CR problem generally for existing and future EDCSR frameworks with no modification to given reconstruction models nor enormous additional rounds of training needed. The CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of acquired image measurements. Subsequently, the CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with a re-sensed initial estimate.

Generic compression ratio adapter for end-to-end data-driven compressive sensing reconstruction frameworks

A data-driven nonuniform subsampling approach for computation-free on-sensor data dimensionality is provided, referred to herein as selective sensing. Designing an on-sensor data dimensionality reduction scheme for efficient signal sensing has long been a challenging task. Compressive sensing is a generic solution for sensing signals in a compressed format. Although compressive sensing can be directly implemented in the analog domain for specific types of signals, many application scenarios require implementation of data compression in the digital domain. However, the computational complexity involved in digital-domain compressive sensing limits its practical application, especially in resource-constrained sensor devices or high-data-rate sensor devices. Embodiments described herein provide a selective sensing framework that adopts a novel concept of data-driven nonuniform subsampling to reduce the dimensionality of acquired signals while retaining the information of interest in a computation-free fashion.

Selective sensing: a data-driven nonuniform subsampling approach for computation-free on-sensor data dimensionality reduction

LAPRAN: a scalable Laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction

A real time end-to-end learning system for a high frame rate video compressive sensing network is described. The slow reconstruction speed of conventional compressive sensing approaches is overcome by directly modeling an inverse mapping from compressed domain to original domain in a single forward propagation. Through processing massive unlabeled video data such a mapping is learned by a neural network using data-driven methods. Systems and methods according to this disclosure incorporate a multi-rate convolutional neural network (CNN) and a synthesizing recurrent neural network (RNN) to achieve real time compression and reconstruction of video data.

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Tempe, AZ, United States of America

Fengbo Ren