Amber: Enabling Precise Full-System Simulation with Detailed Modeling of All SSD Resources

SSDs become a major storage component in modern memory hierarchies, and SSD research demands exploring future simulation-based studies by integrating SSD subsystems into a full-system environment. However, several challenges exist to model SSDs under a full-system simulations; SSDs are composed upon their own complete system and architecture, which employ all necessary hardware, such as CPUs, DRAM and interconnect network. Employing the hardware components, SSDs also require to have multiple device controllers, internal caches and software modules that respect a wide spectrum of storage interfaces and protocols. These SSD hardware and software are all necessary to incarnate storage subsystems under full-system environment, which can operate in parallel with the host system. In this work, we introduce a new SSD simulation framework, SimpleSSD 2.0, namely Amber, that models embedded CPU cores, DRAMs, and various flash technologies (within an SSD), and operate under the full system simulation environment by enabling a data transfer emulation. Amber also includes full firmware stack, including DRAM cache logic, flash firmware, such as FTL and HIL, and obey diverse standard protocols by revising the host DMA engines and system buses of a popular full system simulator's all functional and timing CPU models (gem5). The proposed simulator can capture the details of dynamic performance and power of embedded cores, DRAMs, firmware and flash under the executions of various OS systems and hardware platforms. Using Amber, we characterize several system-level challenges by simulating different types of fullsystems, such as mobile devices and general-purpose computers, and offer comprehensive analyses by comparing passive storage and active storage architectures.Comment: This paper has been accepted at the 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '51), 2018. This material is presented to ensure timely dissemination of scholarly and technical wor

Altruistic Decision-Making for Autonomous Driving with Sparse Rewards

In order to drive effectively, a driver must be aware of how they can expect other vehicles' behaviour to be affected by their decisions, and also how they are expected to behave by other drivers. One common family of methods for addressing this problem of interaction are those based on Game Theory. Such approaches often make assumptions about leaders and followers in an interaction which can result in conflicts arising when vehicles do not agree on the hierarchy, resulting in sub-optimal behaviour. In this work we define a measurement for the incidence of conflicts, Area of Conflict (AoC), for a given interactive decision-making model. Furthermore, we propose a novel decision-making method that reduces this value compared to an existing approach for incorporating altruistic behaviour. We verify our theoretical analysis empirically using a simulated lane-change scenario.Comment: 8 pages, 5 figures, submitted to RSS 2020: Interaction and Decision-Making in Autonomous-Driving Worksho

Automated prior elicitation from large language models for Bayesian logistic regression

We investigate how one can automatically retrieve prior knowledge and use it to improve the sample efficiency of training linear models. This is addressed using the Bayesian formulation of logistic regression, which relies on the specification of a prior distribution that accurately captures the belief the data analyst, or an associated domain expert, has about the values of the model parameters before having seen any data. We develop a broadly applicable strategy for crafting informative priors through the use of Large Language Models (LLMs). The method relies on generating synthetic data using the LLM, and then modelling the distribution over labels that the LLM associates with the generated data. In contrast to existing methods, the proposed approach does not require a substantial time investment from a domain expert and has the potential to leverage access to a much broader range of information. Moreover, our method is straightforward to implement, requiring only the ability to make black-box queries of a pre-trained LLM. The experimental evaluation demonstrates that the proposed approach can have a substantial benefit in some situations, at times achieving an absolute improvement of more than 10% accuracy in the severely data-scarce regime. We show that such gains can be had even when only a small volume of information is elicited from the LLM

Automated prior elicitation from large language models for Bayesian logistic regression

We investigate how one can automatically retrieve prior knowledge and use it to improve the sample efficiency of training linear models. This is addressed using the Bayesian formulation of logistic regression, which relies on the specification of a prior distribution that accurately captures the belief the data analyst, or an associated domain expert, has about the values of the model parameters before having seen any data. We develop a broadly applicable strategy for crafting informative priors through the use of Large Language Models (LLMs). The method relies on generating synthetic data using the LLM, and then modelling the distribution over labels that the LLM associates with the generated data. In contrast to existing methods, the proposed approach does not require a substantial time investment from a domain expert and has the potential to leverage access to a much broader range of information. Moreover, our method is straightforward to implement, requiring only the ability to make black-box queries of a pre-trained LLM. The experimental evaluation demonstrates that the proposed approach can have a substantial benefit in some situations, at times achieving an absolute improvement of more than 10% accuracy in the severely data-scarce regime. We show that such gains can be had even when only a small volume of information is elicited from the LLM

Optimising Network Architectures for Provable Adversarial Robustness

Existing Lipschitz-based provable defences to adversarial examples only cover the l2 threat model. We introduce the first bound that makes use of Lipschitz continuity to provide a more general guarantee for threat models based on any lp norm. Additionally, a new strategy is proposed for designing network architectures that exhibit superior provable adversarial robustness over conventional convolutional neural networks. Experiments are conducted to validate our theoretical contributions, show that the assumptions made during the design of our novel architecture hold in practice, and quantify the empirical robustness of several Lipschitz-based adversarial defence methods

Control of bacterial spot in stone fruit orchards

This project has successfully identified new ways of managing one of the most devastating diseases of stonefruit crops, bacterial spot. Bacterial spot (caused by Xanthomonas arboricola pv. pruni, or Xap) is the most devastating bacterial disease currently affecting Australian stonefruit crops. It has been estimated that bacterial spot affects more than one third of all stonefruit growers in Australia in wet spring/summer seasons, with fruit losses of up to 70% in highly susceptible varieties. This disease significantly reduces the numbers of saleable fruit, and if left untreated can cause long term effects such as reduced tree vigour (leading to poor fruit set and quality), branch loss and tree death in plums. The extent of the problem is so severe that some varieties are unviable for commercial production without a method of bacterial spot control

Why Do Self-Supervised Models Transfer? On the Impact of Invariance on Downstream Tasks

Self-supervised learning is a powerful paradigm for representation learning on unlabelled images. A wealth of effective new methods based on instance matching rely on data-augmentation to drive learning, and these have reached a rough agreement on an augmentation scheme that optimises popular recognition benchmarks. However, there is strong reason to suspect that different tasks in computer vision require features to encode different (in)variances, and therefore likely require different augmentation strategies. In this paper, we measure the invariances learned by contrastive methods and confirm that they do learn invariance to the augmentations used and further show that this invariance largely transfers to related real-world changes in pose and lighting. We show that learned invariances strongly affect downstream task performance and confirm that different downstream tasks benefit from polar opposite (in)variances, leading to performance loss when the standard augmentation strategy is used. Finally, we demonstrate that a simple fusion of representations with complementary invariances ensures wide transferability to all the diverse downstream tasks considered