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Brain-computer interfaces (BCIs) enable direct communication with the brain, providing valuable information about brain function and enabling novel treatment of brain disorders. Our group has been building {\abssys}, a flexible and ultra-low-power processing architecture for BCIs. HALO can process up to 46Mbps of neural data, a significant increase over the interfacing bandwidth achievable by prior BCIs. HALO can also be programmed to support several applications, unlike most prior BCIs. Key to HALO's effectiveness is a hardware accelerator cluster, where each accelerator operates within its own clock domain. A configurable interconnect connects the accelerators to create data flow pipelines that realize neural signal processing algorithms. We have taped out our design in a 12nm CMOS process. The resulting chip runs at 0.88V, per-accelerator frequencies of 3--180MHz, and consumes at most 5.0mW for each signal processing pipeline. Evaluations using electrophysiological data collected from a non-human primate confirm HALO's flexibility and superior performance per watt. 

The security of computers is at risk because of information leaking through their power consumption. Attackers can use advanced signal measurement and analysis to recover sensitive data from this side channel. To address this problem, this paper presents Maya, a simple and effective defense against power side channels. The idea is to use formal control to re-shape the power dissipated by a computer in an application-transparent manner—preventing attackers from learning any information about the applications that are running. With formal control, a controller can reliably keep power close to a desired target function even when runtime conditions change unpredictably. By selecting the target function intelligently, the controller can make power to follow any desired shape, appearing to carry activity information which, in reality, is unrelated to the application. Maya can be implemented in privileged software, firmware, or simple hardware. In this paper, we implement Maya on three machines using privileged threads only, and show its effectiveness and ease of deployment. Maya has already thwarted a newly-developed remote power attack. 

Computing is taking a central role in advancing science, technology, and society, facilitated by increasingly capable systems. Computers are expected to perform a variety of tasks, including life-critical functions, while the resources they require (such as storage and energy) are becoming increasingly limited. To meet expectations, computers use control algorithms that monitor the requirements of the applications they run and reconfigure themselves in response. 

Computer systems are operating in environments where applications are rapidly diversifying while resources like energy and storage are becoming severely limited. These environments demand that computers dynamically manage their resources efciently to deliver the best performance and meet many goals. An important challenge in designing computer resource management systems is that computers are structured in multiple modular layers, such as hardware, operating system, and network. Each layer is complex and designed independently without full knowledge of the other layers. Therefore, computers must have modular resource controllers for each layer that are robust to modeling limitations and the uncertainty of inuence from other layers. Existing designs either rely heavily on ad hoc heuristics or lack modularity. We present a design with multiple Structured Singular Value (SSV) controllers from robust control theory for systematic and efcient computer management. On a challenging computer, we build a two-layer SSV control system that signicantly outperforms state-of-the-art heuristics. 

Resource control in heterogeneous computers built with subsystems from different vendors is challenging. There is a tension between the need to quickly generate local decisions in each subsystem and the desire to coordinate the different subsystems for global optimization. In practice, global coordination among subsystems is considered hard, and current commercial systems use centralized controllers. The result is high response time and high design cost due to lack of modularity. To control emerging heterogeneous computers effectively, we propose a new control framework called Tangram that is fast, glob- ally coordinated, and modular. Tangram introduces a new formal controller that combines multiple engines for optimization and safety, and has a standard interface. Building the controller for a subsystem requires knowing only about that subsystem. As a het- erogeneous computer is assembled, the controllers in the different subsystems are connected hierarchically, exchanging standard co- ordination signals. To demonstrate Tangram, we prototype it in a heterogeneous server that we assemble using components from multiple vendors. Compared to state-of-the-art control, Tangram re- duces, on average, the execution time of heterogeneous applications by 31% and their energy-delay product by 39%.