3 research outputs found
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Overcoming the Intuition Wall: Measurement and Analysis in Computer Architecture
These are exciting times for computer architecture research. Today there is significant demand to improve the performance and energy-efficiency of emerging, transformative applications which are being hammered out by the hundreds for new computing platforms and usage models. This booming growth of applications and the variety of programming languages used to create them is challenging our ability as architects to rapidly and rigorously characterize these applications. Concurrently, hardware has become more complex with the emergence of accelerators, multicore systems, and heterogeneity caused by further divergence between processor market segments. No one architect can now understand all the complexities of many systems and reason about the full impact of changes or new applications.
To that end, this dissertation presents four case studies in quantitative methods. Each case study attacks a different application and proposes a new measurement or analytical technique. In each case study we find at least one surprising or unintuitive result which would likely not have been found without the application of our method
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Anti-Virus in Silicon
Anti-virus (AV) software is fundamentally broken. AV systems today rely on correct functioning of not only the AV software but also the underlying OS and VMM. Thus proper functioning of software AV requires millions of lines of complex code – which houses thousands of bugs – to work correctly. Needless to say, and as evidenced in numerous software AV attacks, effective software AV systems have been difficult to build. At the same time, malware incidents are increasing and there is strong demand for good anti-virus solutions; the software anti-virus market is estimated at close to 8B dollars annually.
In this work we present a new class of robust AV systems called Silicon anti-virus systems. Unlike software AV systems, these systems are lean and mostly implemented in hardware to avoid reliance on complex software, but, like software AV systems, are updatable in the field when new malware is encountered. We describe the first generation of silicon AV that uses simple machine learning techniques with existing performance counter infrastructure. Our published and unpublished work shows that common malware such as viruses and adware, and even zero day exploits can be detected accurately. These systems form a very effective first-line, energy- efficient defense against malware