Industrial Perspectives: The Next Roadblocks in SOC Evolution: On-Chip Storage Capacity and Off-Chip Bandwidth

On-board Compressing of Hyperspectral Images using CCSDS 123

10.14210/cotb.v11n1.p332-336 ◽

2020 ◽

Author(s):

Douglas Santos ◽

Cesar Zeferino ◽

Eduardo Bezerra ◽

Luigi Dilillo ◽

Douglas Melo

Keyword(s):

Image Processing ◽

Storage Capacity ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Embedded Processor ◽

Hyperspectral Image Processing ◽

Systems On Chip ◽

On Chip

A satellite performing hyperspectral image processing requireshigh storage capacity and larger communication bandwidth. Compressionalgorithms, like the CCSDS 123, have been proposed tomitigate these requirements. Considering the constraints associatedto satellites, single-purpose processors have been developedto run these algorithms in Systems-on-Chip (SoC). In this work, weevaluate alternatives to integrate a CCSDS 123 compressor withan embedded processor based on RISC-V and ARM-based architectures.

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Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications

Proceedings ◽

10.3390/proceedings2130852 ◽

2018 ◽

Vol 2 (13) ◽

pp. 852 ◽

Cited By ~ 3

Author(s):

Ievgeniia Maksymova ◽

Christian Steger ◽

Norbert Druml

Keyword(s):

Point Cloud ◽

Storage Capacity ◽

Sensor Data ◽

Future Research ◽

Operating Environment ◽

Compression Algorithms ◽

Computing Unit ◽

Safety Regulations ◽

Electronic Computing ◽

On Chip

Due to specific dynamics of the operating environment and required safety regulations, the amount of acquired data of an automotive LiDAR sensor that has to be processed is reaching several Gbit/s. Therefore, data compression is much-needed to enable future multi-sensor automated vehicles. Numerous techniques have been developed to compress LiDAR raw data; however, these techniques are primarily targeting a compression of 3D point cloud, while the way data is captured and transferred from a sensor to an electronic computing unit (ECU) was left out. The purpose of this paper is to discuss and evaluate how various low-level compression algorithms could be used in the automotive LiDAR sensor in order to optimize on-chip storage capacity and link bandwidth. We also discuss relevant parameters that affect amount of collected data per second and what are the associated issues. After analyzing compressing approaches and identifying their limitations, we conclude several promising directions for future research.

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A Scalable Unsegmented Multiport Memory for FPGA-Based Systems

International Journal of Reconfigurable Computing ◽

10.1155/2015/826283 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Kevin R. Townsend ◽

Osama G. Attia ◽

Phillip H. Jones ◽

Joseph Zambreno

Keyword(s):

High Throughput ◽

High Performance ◽

Storage Capacity ◽

Memory Access ◽

Reconfigurable Architectures ◽

Communication Overhead ◽

Trade Offs ◽

On Chip ◽

Access Patterns ◽

The Cost

On-chip multiport memory cores are crucial primitives for many modern high-performance reconfigurable architectures and multicore systems. Previous approaches for scaling memory cores come at the cost of operating frequency, communication overhead, and logic resources without increasing the storage capacity of the memory. In this paper, we present two approaches for designing multiport memory cores that are suitable for reconfigurable accelerators with substantial on-chip memory or complex communication. Our design approaches tackle these challenges by banking RAM blocks and utilizing interconnect networks which allows scaling without sacrificing logic resources. With banking, memory congestion is unavoidable and we evaluate our multiport memory cores under different memory access patterns to gain insights about different design trade-offs. We demonstrate our implementation with up to 256 memory ports using a Xilinx Virtex-7 FPGA. Our experimental results report high throughput memories with resource usage that scales with the number of ports.

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Gut microbiome a promising target for management of respiratory diseases

Biochemical Journal ◽

10.1042/bcj20200426 ◽

2020 ◽

Vol 477 (14) ◽

pp. 2679-2696

Author(s):

Riddhi Trivedi ◽

Kalyani Barve

Keyword(s):

Respiratory Diseases ◽

New Technology ◽

Bacterial Flora ◽

Systemic Circulation ◽

The Body ◽

Lung Pathology ◽

Promising Target ◽

Current Therapies ◽

Intestinal Microbial Flora ◽

On Chip

The intestinal microbial flora has risen to be one of the important etiological factors in the development of diseases like colorectal cancer, obesity, diabetes, inflammatory bowel disease, anxiety and Parkinson's. The emergence of the association between bacterial flora and lungs led to the discovery of the gut–lung axis. Dysbiosis of several species of colonic bacteria such as Firmicutes and Bacteroidetes and transfer of these bacteria from gut to lungs via lymphatic and systemic circulation are associated with several respiratory diseases such as lung cancer, asthma, tuberculosis, cystic fibrosis, etc. Current therapies for dysbiosis include use of probiotics, prebiotics and synbiotics to restore the balance between various species of beneficial bacteria. Various approaches like nanotechnology and microencapsulation have been explored to increase the permeability and viability of probiotics in the body. The need of the day is comprehensive study of mechanisms behind dysbiosis, translocation of microbiota from gut to lung through various channels and new technology for evaluating treatment to correct this dysbiosis which in turn can be used to manage various respiratory diseases. Microfluidics and organ on chip model are emerging technologies that can satisfy these needs. This review gives an overview of colonic commensals in lung pathology and novel systems that help in alleviating symptoms of lung diseases. We have also hypothesized new models to help in understanding bacterial pathways involved in the gut–lung axis as well as act as a futuristic approach in finding treatment of respiratory diseases caused by dysbiosis.

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