Smart Memory Management (SaMM) For Embedded Systems without MMU

Formal proof of dynamic memory isolation based on MMU

In this work, we present a dynamic memory allocation technique for a novel, horizontally partitioned memory subsystem targeting contemporary embedded processors with a memory management unit (MMU). We propose to replace the on-chip instruction cache with a scratchpad memory (SPM) and a small minicache. Serializing the address translation with the actual memory access enables the memory system to access either only the SPM or the minicache. Independent of the SPM size and based solely on profiling information, a postpass optimizer classifies the code of an application binary into apageableand acacheablecode region. The latter is placed at a fixed location in the external memory and cached by the minicache. The former, the pageable code region, is copied on demand to the SPM before execution. Both the pageable code region and the SPM are logically divided into pages the size of an MMU memory page. Using the MMU's pagefault exception mechanism, a runtime scratchpad memory manager (SPMM) tracks page accesses and copies frequently executed code pages to the SPM before they get executed. In order to minimize the number of page transfers from the external memory to the SPM, good code placement techniques become more important with increasing sizes of the MMU pages. We discuss code-grouping techniques and provide an analysis of the effect of the MMU's page size on execution time, energy consumption, and external memory accesses. We show that by using the data cache as a victim buffer for the SPM, significant energy savings are possible. We evaluate our SPM allocation strategy with fifteen applications, including H.264, MP3, MPEG-4, and PGP. The proposed memory system requires 8% less die are compared to a fully-cached configuration. On average, we achieve a 31% improvement in runtime performance and a 35% reduction in energy consumption with an MMU page size of 256 bytes.

A tutorial overview of the Z8003 and Z8004 microprocessors and the Z8010 and Z8015 memory management units: Part 2: Z8000 family memory management devices

Edge computing is a type of distributed computing that was designed especially for Internet of Things (IoT) users to provide computational resources and data management nearby to users' devices. Introducing edge computing for IoT networks has reduced the bandwidth and latency issue while handling real-time applications. The major benefit of edge computing is that it reduces the communication overhead between IoT user and server. Integrating IoT in our daily life has attracted researchers towards its performance management such as complexity minimization, latency minimization, memory management, energy consumption minimization, etc. The paper focuses on deep reinforcement learning to minimize the computational complexity at IoT user end. The task offloading decision process is designed using Q-Learning that minimizes the system cost and curse of high dimensional data.

International audience

The government has adopted the field of “Internet of Things” as a national strategic project, announcing the Internet of Things master plan to achieve a leading country of hyper-connected digital revolution last May. The government has the promotional strategy of reinforcing the competitiveness in software (SW) sensor components devices, training specialists that will lead Internet of Things (IoT) services and products, and internalizing security for Internet of Things products and services. Internet of Things, thus expected to grow from ₩2 trillion and 300 billion last year to ₩30 trillion by 2020 in the market, refers to the Internet environment where people, things around, data, etc. are all connected to the wired and wireless network to mutually create, collect, share, and utilize information. Internet of Things is the technology and service that includes generating information (sensor) - acquisition (parts, devices) - sharing (Clyde) - utilization (Big Data) - application software. Internet of Things is getting a lot of attention from the public due to the effect of increasing export businesses and jobs and has become a buzzword among businesses. The Internet of Things market has a tendency to grow in the future as it is extended from social infrastructure (utilities, transportation, automation, etc.) and safety management to the consumer sector centered on life services. Sophisticated wireless communication technology is expected to form a huge network connected to all object units as a communication function.

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.

Integrating an on-chip MMU into a highly pipelined architecture

Application of cognitive Internet of Medical Things for COVID-19 pandemic

The initial members of the 88000 family of high-performance 32-bit microprocessor are the 88100 processor and the 88200 cache and memory management unit (CMMU). The processor manipulates integer and floating-point data and initiates instruction and data memory transactions. The CMMU minimizes the latency of main memory requests by maintaining a cache for data transaction and a cache for memory management translations. A typical system consists of one processor and two identical cache chips, one servicing instruction fetch requests, the other servicing data read and write requests. The overall design process for the 88000 family is described, and the integer instructions are discussed. Decisions made with respect to the processor, cache, and software are examined. Some data on the use of the instruction set by the available compilers and the efficiency of the cache and memory systems are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Internet of Things (IoT) has already become an essential part of our daily lives, from mobile phones connected to fitness bands and software applications (apps) controlling the household appliances. Everyday objects like cameras, smart watches, home automation systems, and smartphones are connected to the Internet for sending and receiving data build an IoT network. The advent of IoT in our day-to-day lives has induced variety and volume of data to be present in the shared space. Notwithstanding the advantages of IoT, it also presents a range of potential security risks. Additionally, the traditional IT security models are unable to cope up with the diverse nature of “things” in the IoT. Enterprises are also looking for strategies to meet ever-increasing demands for dynamic, scalable, and adaptive security requirements of IoT. This chapter provides recommendations for leveraging the benefits of traditional secure data handling in the IoT networks with a flavor of dynamism and scalability. Analyzing the current security challenges and constraints posed by the IoT devices, illustration of how devices interchange the sensitive information securely is also provided. The chapter focuses, mainly, on the most relevant available solutions related to the IoT security, e.g., context-based data access control, user identity masking, OAuth authorization, and device-based certificates.

Around 1970 Intel discovered it could put 2,000 transistorsor perhaps a few moreon a single NMOS chip. In retrospect, this may be said to mark the beginning of very large-scale integration (VLSI), ...

The Intel Many Integrated Core (Intel MIC) architecture is Intel's latest design targeted for processing highly parallel workloads. The Intel MIC architecture is implemented on a PCI card, and has its own on-board memory, connected to the host memory through PCI DMA operations. The on-board memory is faster than the one in the host, but it is significantly smaller, requiring the programmer to partition larger computational problems into smaller pieces that can run on a co-processor. However, the Intel MIC architecture features a standard memory management unit (MMU), where the operating system keeps track of the physical memory and manages the mapping from virtual to physical addresses. Thus, the OS running on the many-core unit can transparently move data between the card and the host, similarly how swapping is performed to disk in traditional operating systems. Essentially, the co-processor's memory behaves as another level in the memory hierarchy.

Over the last few years the adequacy of efficient wireless protocols, improved sensors, cheaper processors, established companies developing the necessary management and application software have finally transformed the concept of Internet of Things (IoT) into a master. current. The term 'Internet of Things' also known as 'Internet of Objects' refers to the use of standard Internet protocols to create a secure human-to-thing or thing-to-thing communication environment on embedded networks. Schematically the term describes three things and their harmonious co-operation. The first is the Objects integrated with the sensors, the second is the Data Processing Systems and the third is the Network through which the data is exchanged and the communication is implemented. Essentially, it is a network of physical objects in communication, which enables internet to collect and exchange data between these objects. The rapid development of connected internet devices is estimated to range between 25 and 50 billion euros by the end of 2020. These will include all home appliances, health and fitness equipment, industrial equipment , distributed sensors, cars, and a number of new devices under development. This new technological age, where every small device around us will be connected to the Internet, requires the development of Cyber ​​Security at a rate at least equal to the growth rate of new interconnected devices in order to avoid issues with significant impact. As part of this thesis, cyber security is presented and analyzed in the case of devices that have adopted and utilize IoT technology. The first chapters of the study discuss each aspect of IoT technology and then refer to the models for connecting devices to web processing systems with the introduction of IoT technology. The next chapters refer to the attack surface and the most vulnerable (vulnerable) points of the Internet of Things, as well as the countermeasures that may be undertaken for protection, as well as the methodologies for conducting penetration tests. The postgraduate thesis also includes and analyzes the need to develop a cybersecurity framework that addresses and applies to devices adopting Internet of Things technology, any IoT vulnerabilities, and a case study where the findings of vulnerable devices will be presented. IoT. Security challenges such as privacy and privacy issues are thoroughly documented and analyzed in every respect. Αποστολή σχολίων Ιστορικό Αποθηκeυμένeς Κοινότητα

While the Internet is continuously growing and unavoidable moving towards an ubiquitous internetworking technology, ranging from casual data exchange, home entertainment, security, military, healthcare, transportation, and business applications, to the Internet of Things (IoT) where anything can communicate anywhere, anytime, there is an increasing and urgent need to define global standards for future Internet architectures as well as service definitions. In the Future, the Internet will perhaps become the most exciting entertainment mean. It is very likely that we would be able to make 3D conferencing at home, using holographic projection, spatial sound and haptic transmission. Further, we may no longer use traditional devices such as laptop and desktop computers and cell phones. Monitors and speakers will be completely different ranging from 3D-glasses and headsets to 3D-contact lenses, and in-ear wireless earphones. Keyboard and mouse will disappear as well and will be replaced by biometrics such as voice, iris and finger print recognition, and movement detection. Many projects are being proposed to support and promote the Internet of Things worldwide, ranging from Initiatives (http://www.iot-i.eu/public), Alliances (http://www.ipso-alliance.org/), Forums (http://www.iot-forum.eu/), Consortiums (http://iofthings.org/), Architectures (http://www.iot-a.eu/), Research Clusters (http://www.internet-of-things-research.eu/), and the list goes on. There is seemingly a race towards the leadership on the Internet of things, to the extent that often, driven by "time-to-market" constraints and business requirements, most proposals made so far fail to consider all facets required to deliver the expected services, and often lead to ambiguous and even contradictory definitions of what the Internet of Things is meant to be. For example, some proposals assimilate IoT to Web 3.0, while others claim it is primarily based on RFID and systems alike. Between web semantics and cognitive radio communications, there is a huge gap that needs to be filled out by adequate communication, security, and application software and hardware. Unfortunately, so far, there is no general consensus on the most suitable underlying technology and applications that will help the Internet of Things become a reality. In this presentation, we describe ten of the most challenging hurdles that IoT actors need to face in order to reach their goals. Among these hurdles, we identify the top-ten issues as 1) how to safely migrate from IPv4 to IPv6 given the proliferation of IPv4 devices, 2) Regulation, Pricing and Neutrality of the Future Internet, 3) OAM tools required for accounting and monitoring Future Internet Services, 4) Future Quality of Service definitions and mechanisms, 5) Reliability of Future Internet Services, 6) Future Security Challenges, 7) Scalability of the Internet of Things with its billions of interworked devices, 8) Future Access (local loop) technologies, 9) Future Transport (long distance) telecommunication technologies, and 10) Future End-user Devices and Applications. We aim to briefly explain and highlight the impact of each one of these issues on the service delivery in the context of the Internet of things. We also intend to describe some existing and promising solutions available so far.