Abhinandan Majumdar

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For learning and classification workloads that operate on large amounts of unstructured data with stringent performance constraints, general purpose processor performance scales poorly with data size. In this paper, we present a programmable accelerator for this workload domain. To architect the accelerator, we profile five representative workloads, and(More)
The increasing worldwide concern over the energy consumption of commercial buildings calls for new approaches that analyze scheduled occupant activities and proactively take steps to curb building energy use. As one step in this direction, we propose to automate the scheduling of meetings in a way that uses available meeting rooms in an energy efficient(More)
Semantic text analysis is a technique used in advertisement placement, cognitive databases and search engines. With increasing amounts of data and stringent responsetime requirements, improving the underlying implementation of semantic analysis becomes critical. To this end, we look at Supervised Semantic Indexing (SSI), a recently proposed algorithm for(More)
Applications that use learning and classification algorithms operate on large amounts of unstructured data, and have stringent performance constraints. For such applications, the performance of general purpose processors scales poorly with data size because of their limited support for fine-grained parallelism and absence of software-managed caches. The(More)
Embedded learning applications in automobiles, surveillance, robotics, and defense are computationally intensive, and process large amounts of real-time data. Systems for such workloads have to balance stringent performance constraints within limited power budgets. High performance computer processing units (CPUs) and graphics processing units (GPUs) cannot(More)
Heating ventilation and air-conditioning (HVAC) systems consume a significant portion of the energy within buildings. Current HVAC control systems use simple fixed occupant schedules, while proposed energy optimization schemes do not consider past discomfort in making future energy optimization decisions. We propose a Model-based predictive control (MPC)(More)
Automated building meeting assignment attempts to save meeting room energy by more intelligently assigning meetings to the available rooms. While this approach has shown good potential for particular conditions, it relies on timeconsuming building simulations or model based algorithms that do not scale well to larger problems, and fails to account for(More)
Modern processors can greatly increase energy efficiency through techniques such as dynamic voltage and frequency scaling. Traditional predictive schemes are limited in their effectiveness by their inability to plan for the performance and energy characteristics of upcoming phases. To date, there has been little research exploring more proactive techniques(More)