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Additional resources for Architecture of Computing Systems -- ARCS 2016: 29th International Conference, Nuremberg, Germany, April 4-7, 2016, Proceedings
This light-weight driver provides a lowoverhead and high-performance communication mechanism between the API and PIM. An object-oriented user-level API has been designed, as well, to abstract away the details of the device driver and to facilitate user’s interface. Oﬄoading and coordinating the computations on PIM are initiated by this API. PIM targets execution of medium sized computation kernels having less than a few kilobytes of instructions. rodata sections to PIM’s memory map by the aid from the API.
555x, in turn matching dual-core performance, while consuming 51 % of the area required of a conventional processor back-end and reducing power consumption by 65 %. Future work in CCUs involves branch prediction and a front-end hardware implementation. Acknowledgement. The authors of this work would like to acknowledge the support and funding provided by the Ontario Graduate Scholarship (OGS) program and Ryerson University FEAS. 18 A. Tino and K. Raahemifar References 1. : Neural acceleration for generalpurpose approximate programs.
Several experiments demonstrated that total execution time and delivered bandwidth of the gem5-based model correlate well with the CA model: with low or medium traﬃc pressure, the diﬀerence was less than 1 %, and for high pressure saturating traﬃc the diﬀerence was bounded by 5 %, in all cases. Next, we calibrated the latency of the individual components based on the available data from the literature and the state of the art. The results are shown in Table 1. Table 1. Zero-load latency breakdown of memory accesses from the host and PIM.