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Customization of processor architectures through Instruction Set Extensions (ISEs) is an effective way to meet the growing performance demands of embedded applications. A high-quality ISE generation approach needs to obtain results close to those achieved by experienced designers, particularly for complex applications that exhibit regularity: expert designers are able to exploit manually such regularity in the data flow graphs to generate high-quality ISEs. In this paper, we present ISEGEN, an approach that identifies high-quality ISEs by iterative improvement following the basic principles of the well-known Kernighan-Lin (K-L) min-cut heuristic. Experimental results on a number of MediaBench, EEMBC and cryptographic applications show that our approach matches the quality of the optimal solution obtained by exhaustive search. We also show that our ISEGEN technique is on average 20x faster than a genetic formulation that generates equivalent solutions. Furthermore, the ISEs identified by our technique exhibit 35% more speedup than the genetic solution on a large cryptographic application (AES) by effectively exploiting its regular structure.
In this paper, we study how certain conditions can affect the transformations on the states of the memory of a strict load-store Maurer ISA, when half of the data memory serves as the part of the operating unit.
Simple graph algorithms such as PageRank have recently been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are
Prior work has observed that fetch-directed prefetching (FDIP) is highly effective at covering instruction cache misses. The key to FDIPs effectiveness is having a sufficiently large BTB to accommodate the applications branch working set. In this wor
Our proposed method of random phase-free holography using virtual convergence light can obtain large reconstructed images exceeding the size of the hologram, without the assistance of random phase. The reconstructed images have low-speckle noise in t
The Sphynx project was an exploratory study to discover what might be done to improve the heavy replication of in- structions in independent instruction caches for a massively parallel machine where a single program is executing across all of the cor