Modern embedded system execute a single application or a class of applications repeatedly. A new emerging methodology of designing embedded system utilizes configurable processors where the cache size, associativity, and line size can be chosen by the designer. In this paper, a method is given to rapidly find the L1 cache miss rate of an application. An energy model and an execution time model are developed to find the best cache configuration for the given embedded application. Using benchmarks from Mediabench, we find that our method is on average 45 times faster to explore the design space, compared to Dinero IV while still having 100% accuracy.

Security and reliability in processor based systems are concerns requiring adroit solutions. Security is often compromised by code injection attacks, jeopardizing even 'trusted software'. Reliability is of concern where unintended code is executed in modern processors with ever smaller feature sizes and low voltage swings causing bit flips. Countermeasures by software-only approaches increase code size by large amounts and therefore significantly reduce performance. Hardware assisted approaches add extensive amounts of hardware monitors and thus incur unacceptably high hardware cost. This paper presents a novel hardware/software technique at the granularity of micro-instructions to reduce overheads considerably. Experiments show that our technique incurs an additional hardware overhead of 0.91% and clock period increase of 0.06%. Average clock cycle and code size overheads are just 11.9% and 10.6% for five industry standard application benchmarks. These overheads are far smaller than have been previously encountered.

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The pipelined Multiprocessor System on Chip (MPSoC) paradigm is well suited to the data flow nature of streaming applications. A pipelined MPSoC is a system where processing elements (PEs) are connected in a pipeline. Each PE is implemented using one of a number of processor configurations (configurations differ by instruction sets and cache sizes) available for that PE. The goal is to select a pipelined MPSoC with a mapping of a processor configuration to every PE. To estimate the runtime of a pipelined MPSoC, designers typically perform cycle-accurate simulation of the whole pipelined system. Since the number of possible pipelined implementations can be in the order of billions, estimation methods are necessary.In this paper, we propose two methods to estimate the runtime of a pipelined MPSoC, minimizing the use of slow cycle-accurate simulations. The first method estimates the runtime of the pipelined MPSoC, by performing cycle accurate simulations of individual processor configurations (rather than the whole pipelined system), and then utilizing an analytical model to estimate the runtime of the pipelined system. In the second method, runtimes of individual processor configurations are estimated using an analytical processor model (which uses cycleaccurate simulations of selected configurations, and an equation based on ISA and cache statistics). These estimated runtimes of individual processor configurations are then used to estimate the total runtime of the pipelined system. By evaluating our approach on three benchmarks, we show that the maximum estimation error is 5.91% and 16.45%, with an average estimation error of 2.28% and 6.30% for the first and second method respectively. The time to simulate all the possible pipelined implementations (design points) using cycle-accurate simulator is in the order of years, as design spaces with at least 10 10 design points are considered in this paper. However, the time to simulate all processor configurations individually (first method) takes tens of hours, while the time to simulate a subset of processor configurations and estimate their runtimes (second method) is only a few hours. Once these simulations are done, the runtime of each pipelined implementation can be estimated within milliseconds.

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