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Gabriel Marcano

Publications and source records attributed to Gabriel Marcano.

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Tailor: Altering Skip Connections for Resource-Efficient Inference

Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and off-chip memory utilization and bandwidth requirements. In this paper, we show that skip connections can be optimized for hardware when tackled with a hardware-software codesign approach. We argue that while a network's skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware efficient implementation with minimal to no accuracy loss. We introduce Tailor, a codesign tool whose hardware-aware training algorithm gradually removes or shortens a fully trained network's skip connections to lower their hardware cost. Tailor improves resource utilization by up to 34% for BRAMs, 13% for FFs, and 16% for LUTs for on-chip, dataflow-style architectures. Tailor increases performance by 30% and reduces memory bandwidth by 45% for a 2D processing element array architecture.

cs.CV

Junkyard Computing: Repurposing Discarded Smartphones to Minimize Carbon

1.5 billion smartphones are sold annually, and most are decommissioned less than two years later. Most of these unwanted smartphones are neither discarded nor recycled but languish in junk drawers and storage units. This computational stockpile represents a substantial wasted potential: modern smartphones have increasingly high-performance and energy-efficient processors, extensive networking capabilities, and a reliable built-in power supply. This project studies the ability to reuse smartphones as "junkyard computers." Junkyard computers grow global computing capacity by extending device lifetimes, which supplants the manufacture of new devices. We show that the capabilities of even decade-old smartphones are within those demanded by modern cloud microservices and discuss how to combine phones to perform increasingly complex tasks. We describe how current operation-focused metrics do not capture the actual carbon costs of compute. We propose Computational Carbon Intensity -- a performance metric that balances the continued service of older devices with the superlinear runtime improvements of newer machines. We use this metric to redefine device service lifetime in terms of carbon efficiency. We develop a cloudlet of reused Pixel 3A phones. We analyze the carbon benefits of deploying large, end-to-end microservice-based applications on these smartphones. Finally, we describe system architectures and associated challenges to scale to cloudlets with hundreds and thousands of smartphones.

cs.DC

Keyshuffling Attack for Persistent Early Code Execution in the Nintendo 3DS Secure Bootchain

We demonstrate an attack on the secure bootchain of the Nintendo 3DS in order to gain early code execution. The attack utilizes the block shuffling vulnerability of the ECB cipher mode to rearrange keys in the Nintendo 3DS's encrypted keystore. Because the shuffled keys will deterministically decrypt the encrypted firmware binary to incorrect plaintext data and execute it, and because the device's memory contents are kept between hard reboots, it is possible to reliably reach a branching instruction to a payload in memory. This payload, due to its execution by a privileged processor and its early execution, is able to extract the hash of hardware secrets necessary to decrypt the device's encrypted keystore and set up a persistent exploit of the system.

cs.CR