Social Insect-Inspired Adaptive Hardware

Modern VLSI transistor densities allow large systems to be implemented within a single chip. As technologies get smaller, fundamental limits of silicon devices are reached resulting in lower design yields and post-deployment failures. Many-core systems provide a platform for leveraging the computing resource on offer by deep sub-micron technologies and also offer high-level capabilities for mitigating the issues with small feature sizes. However, designing for many-core systems that can adapt to in-field failures and operation variability requires an extremely large multi-objective optimisation space. When a many-core reaches the size supported by the densities of modern technologies (thousands of processing cores), finding design solutions in this problem space becomes extremely difficult. Many biological systems show properties that are adaptive and scalable. This thesis proposes a self-optimising and adaptive, yet scalable, design approach for many-core based on the emergent behaviours of social-insect colonies. In these colonies there are many thousands of individuals with low intelligence who contribute, without any centralised control, to complete a wide range of tasks to build and maintain the colony. The experiments presented translate biological models of social-insect intelligence into simple embedded intelligence circuits. These circuits sense low-level system events and use this manage the parameters of the many-core's Network-on-Chip (NoC) during runtime. Centurion, a 128-node many-core, was created to investigate these models at large scale in hardware. The results show that, by monitoring a small number of signals within each NoC router, task allocation emerges from the social-insect intelligence models that can self-configure to support representative applications. It is demonstrated that emergent task allocation supports fault tolerance with no extra hardware overhead. The response-threshold decision making circuitry uses a negligible amount of hardware resources relative to the size of the many-core and is an ideal technology for implementing embedded intelligence for system runtime management of large-complexity single-chip systems

Embedded Social Insect-Inspired Intelligence Networks for System-level Runtime Management

Large-scale distributed computing architectures such as, e.g. systems on chip or many-core devices, offer advantages over monolithic or centralised single-core systems in terms of speed, power/thermal performance and fault tolerance. However, these are not implicit properties of such systems and runtime management at software or hardware level is required to unlock these features. Biological systems naturally present such properties and are also adaptive and scalable. To consider how these can be similarly achieved in hardware may be beneficial. We present Social Insect behaviours as a suitable model for enabling autonomous runtime management (RTM) in many-core architectures. The emergent properties sought to establish are self-organisation of task mapping and systemlevel fault tolerance. For example, large social insect colonies accomplish a wide range of tasks to build and maintain the colony. Many thousands of individuals, each possessing relatively little intelligence, contribute without any centralised control. Hence, it would seem that social insects have evolved a scalable approach to task allocation, load balancing and robustness that can be applied to large many-core computing systems. Based on this, a self-optimising and adaptive, yet fundamentally scalable, design approach for many-core systems based on the emergent behaviours of social-insect colonies are developed. Experiments capture decision-making processes of each colony member to exhibit such high-level behaviours and embed these decision engines within the routers of the many-core system

Social-Insect-Inspired Adaptive Task Allocation for Many-Core Systems

Large social insect colonies require a wide range of important tasks to be undertaken to build and maintain the colony. Fortunately, in most nests there are many thousands of workers available to offer their assistance to ensure the expansion and survival of the colony. However, there is a crucial equilibrium between the number of workers performing each task that must not only be maintained but must also continuously adapt to sudden changes in environment and colony need. What is most fascinating is that social insects can sustain this balance without any centralised control and with colony members that have relatively little intelligence when considered on their own. Due to this simplicity and evident scalability it would seem that social insects have evolved an interesting scalable approach to task allocation that could be applied to very large many-core systems. To investigate this we have explored biological models of task allocation in ant colonies and applied this to a 36-core Network on Chip. This paper not only shows that effective decentralised task allocation is achieved, but also that such a scheme can adapt to faults and alter its behaviour to meet soft real-time constraints. Therefore, it is established that social insect inspired intelligence models offer a suitable metaphor and development direction for tackling the challenges introduced by dark silicon and in-field faults in a decentralised and adaptive fashion

Emergent properties of bio-inspired hardware

In this case, the emergent property sought to establish is system- level fault tolerance, the inspiration from biology are social insects (ant colonies), and the hardware system is a many-core computing architecture where application tasks and data need to be allocated transferred and organised. The model of processing elements com- municating amongst each other via a network on chip (NoC) provides a conceptual link with many scalable biological models. Based on this, a self-optimising and adaptive, yet fundamentally scalable, design approach for many-core systems based on the emer- gent behaviours of social-insect colonies are developed. Experiments aim to capture the relevant decision processes made by each member of the colony to exhibit such high-level behaviours and embed these decision engines within the routers of the many-core system. Results with the bespoke 128-core Centurion platform suggest that there is potential for the social insect model as a distributed, embedded intelligence within a many-core system and with the relevant knobs and monitors, such as clock frequency and temperature, to close the loop for emergent autonomous adaptation and fault tolerance

Operating Beyond FPGA Tool Limitations : Nervous Systems for Embedded Runtime Management

Fabrication issues throttle VLSI designs with pes- simistic design constraints and speed-grade device binning nec- essary to avoid failure of devices. We propose that a on chip monitoring system (a Nervous System) can reduce this margin by automatically sensing and reacting to failures and environmental changes at runtime. We demonstrate that pessimistic margins in the FPGA tools allow our test circuit to be overclocked by twice the maximum design tool frequency and run at 50 °C above its maximum operating temperature without error. The Configurable Intelligence Array is introduced as a low-overhead intelligence platform and used for a prototype neural circuit that can close the loop between a timing-fault detector and a programmable Phase Locked Loop (PLL) oscillator

MPSoCs for Reconfigurable Modular Spacecraft

Modular, reconfigurable spacecraft offer a new approach to extending mission capability and maximising the lifetime of a spacecraft. Future uses of space robotics such as in-orbit construction and servicing allow faulty or obsolete parts of a modular spacecraft to be replaced by servicer spacecraft that dock with their targets and perform upgrades and maintenance. Such manoeuvres will require a high degree of autonomy from both platforms and thus will need to leverage high-performance onboard computing for both the robotic control and manipulation of service spacecraft but also for managing Thales Alenia Space in the UK (TAS UK) and The University of York (UoY) are involved in projects towards this goal and are collaborating to research autonomous network reconfiguration and fault tolerance of the onboard network based on existing space technology (SpaceWire, SpaceFibre). Both organisations have identified FPGA based MPSoCs as a solution for providing the high-performance computing that autonomous robotic systems require, using the FPGA fabric for mission-phase related hardware accelerators (e.g. vision soft co- processors) that can be swapped as the construction or maintenance task demands. In this presentation we will describe the modular spacecraft avionics unit that TAS UK is developing for the H2020 MOSAR project. This is based on the Xilinx Ultrascale+ MPSoC and uses the “big-little” architecture to provide a split between the spacecraft module’s mission functionality (executing on the “big” quad-core A53) and the support functions to provide: the communication network, module-to-module docking management and the module power management functions of the spacecraft (implemented on the “little” dual-core R5 cores). Details on our development of an AXI4 compatible SpaceWire and RMAP IP core will also be included. RMAP forms an important part of the MOSAR fault management strategy and this core allows processor-transparent RMAP access to the full MPSoC address range, with automatic DMA descriptors for all other SpaceWire traffic. The AXI4 interface simply allows it to be dropped into any Ultrascale+, Zynq 7000 and NG-ultra based design and several configuration options allow options such as SpW front end type (oversampling /clock recovery) and output data path width (32-bit/16bit) to be selected. We will also present details of research by the University of York on using RMAP in a MPSoC environment. Access to the full address space of a MPSoC via RMAP brings security and fault management concerns to complex SoCs and hardware security based approaches (e.g. ARM’s TrustZone) could be used in future MP- SoC architectures to protect against damage by either corrupt RMAP packets, damage from failure modes of RMAP initiators or malicious/compromised spacecraft modules. To tackle autonomy challenges UoY is cur- rently developing a reasoner based, reconfigurable modular robotic platform that can cope with uncertain environments that arise in space applications using FPGA based MPSoC and soft-processor technologies. MOSAR has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement No. 821996. Part of this work is funded by EPSRC and Innovate UK under grant KTP12066

Nitrate Removal Performance of Denitrifying Woodchip Bioreactors in Tropical Climates

In Australia, declining water quality in the Great Barrier Reef (GBR) is a threat to its marine ecosystems and nitrate (NO3−) from sugar cane-dominated agricultural areas in the coastal catchments of North Queensland is a key pollutant of concern. Woodchip bioreactors have been identified as a potential low-cost remediation technology to reduce the NO3− runoff from sugar cane farms. This study aimed to trial different designs of bioreactors (denitrification walls and beds) to quantify their NO3− removal performance in the distinct tropical climates and hydrological regimes that characterize sugarcane farms in North Queensland. One denitrification wall and two denitrification beds were installed to treat groundwater and subsurface tile-drainage water in wet tropics catchments, where sugar cane farming relies only on rainfall for crop growth. Two denitrification beds were installed in the dry tropics to assess their performance in treating irrigation tailwater from sugarcane. All trialled bioreactors were effective at removing NO3−, with the beds exhibiting a higher NO3− removal rate (NRR, from 2.5 to 7.1 g N m−3 d−1) compared to the wall (0.15 g N m−3 d−1). The NRR depended on the influent NO3− concentration, as low influent concentrations triggered NO3− limitation. The highest NRR was observed in a bed installed in the dry tropics, with relatively high and consistent NO3− influent concentrations due to the use of groundwater, with elevated NO3−, for irrigation. This study demonstrates that bioreactors can be a useful edge-of-field technology for reducing NO3− in runoff to the GBR, when sited and designed to maximise NO3− removal performance

Comparing Outcomes with Bone Marrow or Peripheral Blood Stem Cells as Graft Source for Matched Sibling Transplants in Severe Aplastic Anemia across Different Economic Regions

Bone marrow (BM) is the preferred graft source for hematopoietic stem cell transplantation (HSCT) in severe aplastic anemia (SAA) compared to mobilized peripheral blood stem cells (PBSC). We hypothesized that this recommendation may not apply to those regions where patients present later in their disease course, with heavier transfusion load and with higher graft failure rates. Patients with SAA who received HSCT from an HLA-matched sibling donor from 1995 to 2009 and reported to the Center for International Blood and Marrow Transplant Research or the Japan Society for Hematopoietic Cell Transplantation were analyzed. The study population was categorized by gross national income per capita (GNI) and region/countries into four groups. Groups analyzed were high income countries (HIC), which were further divided into US-Canada (N=486) and other HIC (N=1264), upper middle-income (UMIC) (N=482), and combined lower middle, low income countries (LM-LIC) (N=142). In multivariate analysis, overall survival (OS) was highest with BM as graft source in HIC compared to PBSC in all countries or BM in UMIC or LM-LIC (p<0.001). There was no significant difference in OS between BM and PBSC in UMIC (p=0.32) or LM-LIC (p=0.23). In LM-LIC the 28-day neutrophil engraftment was higher with PBSC compared to BM (97% vs. 77%, p<0.001). Chronic GVHD was significantly higher with PBSC in all groups. Whereas BM should definitely be the preferred graft source for HLA-matched sibling HSCT in SAA, PBSC may be an acceptable alternative in countries with limited resources when treating patients at high risk of graft failure and infective complications