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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. 

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%.