Parth Malani

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In the deep submicron era, thermal hot spots and large temperature gradients significantly impact system reliability, performance, cost and leakage power. As the system complexity increases, it is more and more difficult to perform thermal management in a centralized manner because of state explosion and the overhead of monitoring the entire chip. In this(More)
In this paper we present three efficient DVS techniques for an MPEG decoder. Their energy reduction is comparable to that of the optimal solution. A workload prediction model is also developed based on the block level statistics of each MPEG frame. Compared with previous works, the new model exhibits a remarkable improvement in accuracy of the prediction.(More)
The computational workload of some real-time applications varies significantly during runtime, which makes the task scheduling and power management a challenge. One of the major influences to the workload of an application is the selection of conditional branches which may activate or deactivate a large set of operations. Focusing on real-time applications(More)
In this paper, we propose a scheduling algorithm to minimize the resource contentions and the processing latency for applications running on a multiprocessor system-on-chip (MPSoC) platform. The scheduling algorithm is applied on an MPSoC MPEG decoder to improve the system performance. Application specific task partition and mapping techniques are further(More)
In this paper, we focus on power optimization of real-time applications with conditional execution running on a dynamic voltage scaling (DVS) enabled multiprocessor system. The targeted system consists of heterogeneous processing elements with non-negligible inter-processor communication delay and energy. Given a conditional task graph (CTG), we have(More)
Neuromorphic computing systems refer to the computing architecture inspired by the working mechanism of human brains. The rapidly reducing cost and increasing performance of state-of-the-art computing hardware allows large-scale implementation of machine intelligence models with neuromorphic architectures and opens the opportunity for new applications. One(More)
Many real time applications demonstrate uncertain workload that varies during runtime. One of the major influences of the workload fluctuation is the selection of conditional branches which activate or deactivate a set of instructions belonging to a task. In this work, we capture the workload dynamics using Conditional Task Graph (CTG) and propose a set of(More)
This work focuses on power optimization of realtime applications with conditional execution running on a dynamic voltage scaling (DVS) enabled multiprocessor system. A novel algorithm is proposed that performs simultaneous task mapping and ordering followed by task stretching of a conditional task graph (CTG). The algorithm minimizes the mathematical(More)
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