111 research outputs found

    A Benes Based NoC Switching Architecture for Mixed Criticality Embedded Systems

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    Multi-core, Mixed Criticality Embedded (MCE) real-time systems require high timing precision and predictability to guarantee there will be no interference between tasks. These guarantees are necessary in application areas such as avionics and automotive, where task interference or missed deadlines could be catastrophic, and safety requirements are strict. In modern multi-core systems, the interconnect becomes a potential point of uncertainty, introducing major challenges in proving behaviour is always within specified constraints, limiting the means of growing system performance to add more tasks, or provide more computational resources to existing tasks. We present MCENoC, a Network-on-Chip (NoC) switching architecture that provides innovations to overcome this with predictable, formally verifiable timing behaviour that is consistent across the whole NoC. We show how the fundamental properties of Benes networks benefit MCE applications and meet our architecture requirements. Using SystemVerilog Assertions (SVA), formal properties are defined that aid the refinement of the specification of the design as well as enabling the implementation to be exhaustively formally verified. We demonstrate the performance of the design in terms of size, throughput and predictability, and discuss the application level considerations needed to exploit this architecture

    RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults

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    To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.g., due to wear out) during the run-time operation. However, the impact of permanent faults in SNN chips and the respective mitigation techniques have not been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel methodology to mitigate permanent faults in the compute engine of SNN chips without requiring additional retraining, thereby significantly cutting down the design time and retraining costs, while maintaining the throughput and quality. The key ideas of our RescueSNN methodology are (1) analyzing the characteristics of SNN under permanent faults; (2) leveraging this analysis to improve the SNN fault-tolerance through effective fault-aware mapping (FAM); and (3) devising lightweight hardware enhancements to support FAM. Our FAM technique leverages the fault map of SNN compute engine for (i) minimizing weight corruption when mapping weight bits on the faulty memory cells, and (ii) selectively employing faulty neurons that do not cause significant accuracy degradation to maintain accuracy and throughput, while considering the SNN operations and processing dataflow. The experimental results show that our RescueSNN improves accuracy by up to 80% while maintaining the throughput reduction below 25% in high fault rate (e.g., 0.5 of the potential fault locations), as compared to running SNNs on the faulty chip without mitigation.Comment: Accepted for publication at Frontiers in Neuroscience - Section Neuromorphic Engineerin

    Comprehensive Evaluation of Supply Voltage Underscaling in FPGA on-Chip Memories

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    In this work, we evaluate aggressive undervolting, i.e., voltage scaling below the nominal level to reduce the energy consumption of Field Programmable Gate Arrays (FPGAs). Usually, voltage guardbands are added by chip vendors to ensure the worst-case process and environmental scenarios. Through experimenting on several FPGA architectures, we measure this voltage guardband to be on average 39% of the nominal level, which in turn, delivers more than an order of magnitude power savings. However, further undervolting below the voltage guardband may cause reliability issues as the result of the circuit delay increase, i.e., start to appear faults. We extensively characterize the behavior of these faults in terms of the rate, location, type, as well as sensitivity to environmental temperature, with a concentration of on-chip memories, or Block RAMs (BRAMs). Finally, we evaluate a typical FPGA-based Neural Network (NN) accelerator under low-voltage BRAM operations. In consequence, the substantial NN energy savings come with the cost of NN accuracy loss. To attain power savings without NN accuracy loss, we propose a novel technique that relies on the deterministic behavior of undervolting faults and can limit the accuracy loss to 0.1% without any timing-slack overhead.Peer ReviewedPostprint (author's final draft

    High-Performance DRAM System Design Constraints and Considerations

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    The effects of a realistic memory system have not received much attention in recent decades. Often, the memory controller and DRAMs are modeled as a fixed-latency or random-latency system, which leads to simulations that are less accurate. As more cores are added to each die and CPU clock rates continue to outpace memory access times, the gap will only grow wider and simulation results will be less accurate. This thesis proposes to look at the way a memory controller and DRAM system work and attempt to model them accurately in a simulator. It will use a simulated Alpha 21264 processor in conjunction with a full system simulator and memory system simulator. Various SPEC06 benchmarks are used to look at runtimes. The process of mapping a memory location to a physical location, the algorithm for choosing the ordering of commands to be sent to the DRAMs and the method of managing the row buffers are examined in detail. We find that the choice in these algorithms and policies can affect application runtime by up to 200% or more. It is also shown that energy use can vary by up to 300% by changing changing the address mapping policy. These results show that it is important to look at all the available policies to optimize the memory system for the type of workload that a machine will be running. No single policy is best for every application, so it is important to understand the interaction of the application and the memory system to improve performance and reduce the energy consumed

    A Comprehensive Test and Diagnostic Strategy for TCAMs

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    Content addressable memories (CAMs) are gaining popularity with computer networks. Testing costs of CAMs are extremely high owing to their unique configuration. In this thesis, a fault analysis is carried out on an industrial ternary CAM (TCAM) design, and search path test algorithms are designed. The proposed algorithms are able to test the TCAM array, multiple-match resolver (MMR), and match address encoder (MAE). The tests represent a 6x decrease in test complexity compared to existing algorithms, while dramatically improving fault coverage

    A Benes̆ Based NoC Switching Architecture for Mixed Criticality Embedded Systems

    Get PDF
    Multi-core, Mixed Criticality Embedded (MCE) real-time systems require high timing precision and predictability to guarantee there will be no interference between tasks. These guarantees are necessary in application areas such as avionics and automotive, where task interference or missed deadlines could be catastrophic, and safety requirements are strict. In modern multi-core systems, the interconnect becomes a potential point of uncertainty, introducing major challenges in proving behaviour is always within specified constraints, limiting the means of growing system performance to add more tasks, or provide more computational resources to existing tasks. We present MCENoC, a Network-on-Chip (NoC) switching architecture that provides innovations to overcome this with predictable, formally verifiable timing behaviour that is consistent across the whole NoC. We show how the fundamental properties of Benes networks benefit MCE applications and meet our architecture requirements. Using SystemVerilog Assertions (SVA), formal properties are defined that aid the refinement of the specification of the design as well as enabling the implementation to be exhaustively formally verified. We demonstrate the performance of the design in terms of size, throughput and predictability, and discuss the application level considerations needed to exploit this architecture

    RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults

    Get PDF
    To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.g., due to wear out) during the run-time operation. However, the impact of permanent faults in SNN chips and the respective mitigation techniques have not been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel methodology to mitigate permanent faults in the compute engine of SNN chips without requiring additional retraining, thereby significantly cutting down the design time and retraining costs, while maintaining the throughput and quality. The key ideas of our RescueSNN methodology are (1) analyzing the characteristics of SNN under permanent faults; (2) leveraging this analysis to improve the SNN fault-tolerance through effective fault-aware mapping (FAM); and (3) devising lightweight hardware enhancements to support FAM. Our FAM technique leverages the fault map of SNN compute engine for (i) minimizing weight corruption when mapping weight bits on the faulty memory cells, and (ii) selectively employing faulty neurons that do not cause significant accuracy degradation to maintain accuracy and throughput, while considering the SNN operations and processing dataflow. The experimental results show that our RescueSNN improves accuracy by up to 80% while maintaining the throughput reduction below 25% in high fault rate (e.g., 0.5 of the potential fault locations), as compared to running SNNs on the faulty chip without mitigation. In this manner, the embedded systems that employ RescueSNN-enhanced chips can efficiently ensure reliable executions against permanent faults during their operational lifetime

    ТЕСТИРОВАНИЕ ОЗУ НА ОСНОВЕ АДАПТИВНОГО СЖАТИЯ ВЫХОДНЫХ ДАННЫХ

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    Предлагается новая концепция неразрушающего тестирования оперативных запоминающих устройств (ОЗУ) на базе адаптивного сжатия выходных данных. Данная концепция основывается на использовании характеристики ОЗУ на базе адаптивного сжатия выходных данных, получаемой путем суммирования по модулю два всех адресов ячеек памяти, которые содержат единичные значения. Показывается, что эта характеристика может быть использована в качестве эталонной сигнатуры при тестировании ОЗУ. Рассматриваются основные свойства предлагаемых новых неразрушающих тестов, основанных на применении адаптивного сжатия выходных данных
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