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In the wake of dwindling Moore's Law, to address the rapidly increasing complexity and cost of fabricating large-scale, monolithic systems-on-chip (SoCs), the industry has adopted dis-aggregation as a solution, wherein a large monolithic SoC is partitioned into multiple smaller chiplets that are then assembled into a large system-in-package (SiP) using advanced packaging substrates such as silicon interposer. For such interposer-based SiPs, there is a push to realize on-interposer inter-chiplet communication bandwidth of multi-Tb/s and end-to-end communication latency of no more than 10 ns. This push comes as the natural progression from some recent prior works on SiP design, and is driven by the proliferating bandwidth demand of modern data-intensive workloads. To meet this bandwidth and latency goal, prior works have focused on a potential solution of using the silicon photonic interposer (SiPhI) for integrating and interconnecting a large number of chiplets into an SiP. Despite the early promise, the existing designs of on-SiPhI interconnects still have to evolve by leaps and bounds to meet the goal of multi-Tb/s bandwidth. However, the possible design pathways, upon which such an evolution can be achieved, have not been explored in any prior works yet. In this paper, we have identified several design pathways that can help evolve on-SiPhI interconnects to achieve multi-Tb/s aggregate bandwidth. We perform an extensive link-level and system-level analysis in which we explore these design pathways in isolation and in different combinations of each other. From our link-level analysis, we have observed that the design pathways that simultaneously enhance the spectral range and optical power budget available for wavelength multiplexing can render aggregate bandwidth of up to 4 Tb/s per on-SiPhI link. We also show that such high-bandwidth on-SiPhI links can substantially improve the performance and energy-efficiency of the state-of-the-art CPU and GPU chiplets based SiPs. [ABSTRACT FROM AUTHOR]

Background: With the advancement in technology the amount of data generated, in almost every sphere of life, is increasing exponentially. This enormous amount of data needs new powerful tools for analysis and inference drawing. One such process is data mining which is the automated extraction of hidden, previously unknown, and useful knowledge from big data. Data mining is crucial as conventional strategies cannot keep up with the analysis of rapidly accumulating data and they are also inflexible in the wake of new challenges. Animal sciences are no exception to the changing scenario, especially when animal farms are quickly becoming more data intensive. The amount of data generated on the farms is also growing exponentially as farms become more intensive and mechanized. There is thus a need to utilize the knowledge of multidisciplinary fields like advanced statistics, artificial intelligence, machine learning, and database management, for revamping animal sciences. Artificial neural networks (ANNs) offer a lot of promise in this direction since they are motivated by the distributed, massively parallel computation in the brain. ANNs are powerful machine learning tools that offer multiple advantages for data mining over traditional techniques in being fast, accurate, self-organizing, robust, and highly accepting of noisy and imprecise data. Neural networks are being applied successfully for a myriad of supervised and unsupervised learning applications to draw useful hitherto unknown inferences, patterns, and relationships. Neural networks have been used successfully for pattern recognition, clustering, forecasting, prediction, and classification in animal sciences due to their capacity to learn from data, their nonparametric nature, and their ability to generalize well. Today ANN computing is a major element within any data mining tool kit. Popular methods used for neural network computing include feed-forward networks, feedback networks, and self-organization networks. ANN also offers powerful and distributed computing architecture, especially under a scenario where the data are readily available in significant quantity. Short conclusion: This paper gives an overview of ANN and their applications in animal sciences and reviews major research conducted in this new and exciting area of artificial intelligence. Research in many aspects of ANN in Animal Sciences has been conducted globally although there is scope for more research in aspects of animal health, monitoring, breeding as well as nutrition. [ABSTRACT FROM AUTHOR]

On-chip photonics is a disruptive technology, and such NoCs are superior to traditional electrical NoCs in terms of latency, power, and bandwidth. Hence, researchers have proposed a wide variety of optical networks for multicore processors. The high bandwidth and low latency features of photonic NoCs have led to the overall improvement in the system performance. However, there are very few proposals that discuss the usage of optical interconnects in Graphics Processor Units (GPUs). GPUs can also substantially gain from such novel technologies, because they need to provide significant computational throughput without further stressing their power budgets. The main shortcoming of optical networks is their high static power usage, because the lasers are turned on all the time by default, even when there is no traffic inside the chip, and thus sophisticated laser modulation schemes are required. Such modulation schemes base their decisions on an accurate prediction of network traffic in the future. In this article, we propose an energy-efficient and scalable optical interconnect formodern GPUs called GPUOPT that smartly creates an overlay network by dividing the symmetric multiprocessors (SMs) into clusters. It furthermore has separate sub-networks for coherence and non-coherence traffic. To further increase the throughput, we connect the of-chip memory with optical links as well. Subsequently, we show that traditional laser modulation schemes (for reducing static power consumption) that were designed for multicore processors are not that effective for GPUs. Hence, there was a need to create a bespoke scheme for predicting the laser power usage in GPUs. Using this set of techniques, we were able to improve the performance of a modern GPU by 45% as compared to a state-of-the-art electrical NoC. Moreover, as compared to competing optical NoCs for GPUs, our scheme reduces the laser power consumption by 67%, resulting in a net 65% reduction in ED2 for a suite of Rodinia benchmarks. [ABSTRACT FROM AUTHOR]

Numerous challenges present themselves when scaling traditional on-chip electrical networks to large manycore processors. Some of these challenges include high latency, limitations on bandwidth, and power consumption. Researchers have therefore been looking for alternatives. As a result, on-chip nanophotonics has emerged as a strong substitute for traditional electrical NoCs. As of 2017, on-chip optical networks have moved out of textbooks and found commercial applicability in short-haul networks such as links between servers on the same rack or between two components on the motherboard. It is widely acknowledged that in the near future, optical technologies will move beyond research prototypes and find their way into the chip. Optical networks already feature in the roadmaps of major processor manufacturers and most on-chip optical devices are beginning to show signs of maturity. This article is designed to provide a survey of on-chip optical technologies covering the basic physics underlying the operation of optical technologies, optical devices, popular architectures, power reduction techniques, and applications. The aim of this survey article is to start from the fundamental concepts and move on to the latest in the field of on-chip optical interconnects. [ABSTRACT FROM AUTHOR]

In this article, we propose using optical networks-on-chip (NoCs) to design cache access protocols for large shared L2 caches. We observe that the problem is unique because optical networks have very low latency, and in principle all of the cache banks are very close to each other. A naive approach is to broadcast a request to a set of banks that might possibly contain the copy of a block. However, this approach is wasteful in terms of energy and bandwidth. Hence, we propose a set of novel schemes that create a set of virtual networks (overlays) of cache banks over a physical optical NoC. We search for a block inside each overlay using a combination of multicast and unicast messages. We first propose two simple protocols: TSI and Broadcast. The former uses unicast messages, and the latter uses multicast messages. We subsequently propose an improved scheme, OP_BCAST, that combines the best of TSI and Broadcast, and mainly uses restricted multicastmessages. Then we propose a set of novel hardware structures for creating and managing overlays, for efficiently locating blocks in the overlay, and for implementing dynamically changing overlays with OP_BCAST. The performance of the TSI scheme is within 2% to 3% of a broadcast scheme, and it is faster than traditional schemes with electrical networks by 26%. Compared to the broadcast scheme, it reduces the number of accesses, and consequently the dynamic energy of the caches by 6% to 8% . OP_BCAST is 34% faster than the best solutions with copper-based NoCs; moreover, it reduces the dynamic energy for cache access by 33% compared to the TSI scheme. [ABSTRACT FROM AUTHOR]