Silicon photonic networks are revolutionizing computing systems by improving the energy efficiency, bandwidth, and latency of data movements. Optical modulators, such as microresonators (MRs) and Mach–Zehnder interferometers (MZIs), are the basic building blocks of silicon photonic networks. This paper proposes a SPICE-compatible electro-optical co-simulation model, basic optical switch integration model (BOSIM), to systematically study optical modulators using PN, PIN, and metal–insulator–silicon (MIS) capacitor device technologies. BOSIM holistically models both transient and steady state properties, such as switching speed, power, transmission spectrum, area, and carrier distribution. BOSIM is validated by the measured data from eight research groups and companies. Compared to MRs, BOSIM shows MZIs are fast, with a high extinction ratio and large bandwidth but in the sacrifice of loss, energy, and area. Using a PIN diode over a PN diode can save area, but retain the loss and energy, while an MIS capacitor has the shock response of carrier distribution in a narrow range and is marginalized gradually. For instance, an MZI can achieve a$2.5 {\times }$bit rate,$6.06{\times }$extinction ratio,$71.04 {\times }\,\,3$-dB bandwidth, but costs at least$1.93 {\times }$passing loss,$1.46 {\times }$energy consumption, and$16.67 {\times }$area, compared with MR.

The advancement of silicon photonics promises integrated optical switches to provide high-bandwidth, low-latency, and low-power communications in data centers. An optical switch’s loss limits its scale and affects the energy efficiency of the switch system. In this paper, we present cross-layer optical switch optimization (CLOSO), a cross-layer optimization (CLO) framework, based on not only photonic device models at the physical layer but also optical switch models at the fabric layer. With the proposed framework, optimal losses of optical switches can be evaluated efficiently, and the corresponding losses and design parameters of photonic devices can be obtained. Using CLOSO, we optimize four categories of integrated optical switches, Crossbar, PILOSS, DRAGON, and FODON, and compare them regarding their optimal worst-case loss with variation of the switch scale and data rate of signals. Furthermore, system-level evaluations of the optimized optical switches are performed, demonstrating a significant improvement of energy efficiency from the CLO. For instance, CLOSO helps to reduce the energy consumption of a 64-port DRAGON and FODON to as low as 6 pJ/bit and that of a 128-port DRAGON and FODON to as low as 10 pJ/bit. The investigation of 128-port switches also shows the necessity of adaptive power control on lasers for high-radix integrated optical switches. Through quantitative analyses and comparisons, CLOSO shows the capability of facilitating initial design exploration of optical switches and paves the way to fair evaluations and comparisons of switch systems in data centers.

Rack-scale computing systems are promising to undertake the emerging large-scale applications by distributing massive tasks to processing cores. The communication and coordination efficiency of these tasks and resources directly affect the system performance and energy consumption. Silicon photonic interconnects are expected to address the communication and system power consumption challenges imposed on rack-scale systems. However, the control for optical interconnects can cause server performance degradation if not properly designed, especially for the complicated and time-consuming multidomain networks. In this paper, we study the optical interconnects for rack-scale computing systems and propose a new communication flow and control scheme for the efficient coordination of distributed resources. Particularly, we first propose a forward propagation strategy that parallels the path reservation process with the distributed tasks connection setup. Second, we develop a pre-emptive chain feedback (PCF) scheme to optimize multidomain path reservation. The PCF scheme pre-emptively allocates network resources with the help of multicell reservation window and quickly releases resources with a feedback mechanism. This solution increases the network resources utilization and task coordination efficiency while minimizing path reservation overheads. Comparing to the baseline InfiniBand network fabric and handshake scheme, PCF can improve network throughput greatly under uniform and hotspot traffic patterns. Realistic benchmark results show that the PCF scheme on average reduces 52% and 60% energy consumption per unit system performance than InfiniBand and the handshake scheme for a 256-node rack system.

In recent years, optical interconnection networks have been proposed in order to achieve the ultrahigh bandwidth and low latency requirements for inter/intra-chip communication. In these optical interconection networks, series of basic optical elements are employed. Via these series of optical elements, the intrinsic crosstalk noise is generated. With a large scale of these optical elements, the signal-to-noise ratio (SNR) of an optical interconnect can be reduced by this crosstalk noise. In this paper, we utilize the adaptive power control (APC) to enhance the SNR under the crosstalk noise constraints. APC has been known to save energy and reduce power consumption. We apply this technique in one of the inter/intra-chip optical interconnect called I2CON. A new cluster design, namely the Beam cluster, is also introduced. Results have demonstrated that the APC can help to reduce crosstalk noise, hence, the overall SNR is improved. Comparison results have also indicated the further improvement of SNR in I2CON using Beam cluster when APC is applied.

Rapidly evolving embedded applications continuously demand more functionalities and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Learning-based methods are adaptive to dynamic conditions and show great potential for runtime power management. However, with the ever-increasing complexity of both hardware and software, it is a challenging issue for a learning agent to explore the state-action space sufficiently and quickly find an efficient management policy. In this paper, we propose a reinforcement learning-based multi-device collaborative power management approach to address this issue. Multiple devices with different runtime conditions can acquire related knowledge during the learning process. Efficient knowledge sharing among these devices can potentially accelerate the learning process and improve the quality of the learned policies. We integrate the proposed method with dynamic voltage and frequency scaling on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve up to a $7 \times$ speedup and 10% energy reduction compared with state-of-the-art learning-based approaches.

Rapidly evolving embedded applications continuously demand more functionality and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Although learning-based runtime power management can adapt to dynamic conditions, it is a challenging issue to quickly find an efficient management policy under ever-increasing hardware and software complexity. In this work, we propose a multi-device collaborative power management approach to address this issue. The collaborative power management periodically shares knowledge among multiple devices to accelerate the learning process and improve the quality of learned policies. We integrate the proposed method with dynamic voltage and frequency scaling (DVFS) on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve on average 8x speedup and 10% energy saving compared with state-of-the-art learning-based approaches.

The increasing demand for more computational power from scientific computing, big data processing, and machine learning is pushing the development of HPC (high-performance computing) systems. As the basic HPC building blocks, modularized server racks with a large number of multicore nodes are facing performance and energy efficiency challenges. This paper proposes RSON, an optical network for rack-scale computing systems. RSON connects processor cores, caches, local memories, and remote memories through a novel inter/intra-chip silicon photonic network architecture. We develop a low-latency scalable channel partition and low-power dynamic path priority control scheme for RSON. Experimental results show that RSON can help rack-scale computing systems achieve up to 6.8X higher performance under the same energy consumption than state-of-the-art systems under the latest APEX (application performance at extreme scale) benchmarks.

The power delivery system (PDS), which plays a crucial role in guaranteeing the proper functionality of computing systems, has been a serious constraint on performance due to its significant power loss, especially for high-performance many-core processors. As the PDS design is usually optimized to provide power to the target chip at its best performance level, the energy efficiency can be notably degraded when the workload of the processors is highly dynamic. Therefore, dynamically adjusting the PDS to adapt to the run-time chip workloads are expected to be beneficial. In addition, the introduction of on-chip voltage regulators (VRs) has also significantly broadened the design space of PDS. In this paper, we present a workload-aware quantized power management scheme to dynamically manage the PDS in order to improve system energy efficiency. VRs at different stages are scheduled as online or offline based on chip workload estimation and prediction. The simulation results show that with the proposed scheme, a hybrid PDS with on/off-chip VRs can achieve 74.6% overall efficiency on average, 12.7% higher than a conventional PDS with one off-chip VR. The proposed scheme also shows its potential advantage in improving system energy efficiency with large-scale many-core processors and with more advanced processor technology nodes.

Energy-efficiency is becoming increasingly important to modern computing systems with multi-/many-core architectures. Dynamic Voltage and Frequency Scaling (DVFS), as an effective low-power technique, has been widely applied to improve energy-efficiency in commercial multi-core systems. However, due to the large number of cores and growing complexity of emerging applications, it is difficult to efficiently find a globally optimized voltage/frequency assignment at runtime. In order to improve the energy-efficiency for the overall multicore system, we propose an online DVFS control strategy based on core-level Modular Reinforcement Learning (MRL) to adaptively select appropriate operating frequencies for each individual core. Instead of focusing solely on the local core conditions, MRL is able to make comprehensive decisions by considering the running-states of multiple cores without incurring exponential memory cost which is necessary in traditional Monolithic Reinforcement Learning (RL). Experimental results on various realistic applications and different system scales show that the proposed approach improves up to 28% energy-efficiency compared to the recent individual-RL approach.

The memory wall problem is due to the imbalanced developments and separation of processors and memories. It is becoming acute as more and more processor cores are integrated into a single chip and demand higher memory bandwidth through limited chip pins. Optical memory interconnection network (OMIN) promises high bandwidth, bandwidth density, and energy efficiency, and can potentially alleviate the memory wall problem. In this paper, we propose an optical inter/intra-chip processor-memory communication architecture, called MOCA. Experimental results and analysis show that MOCA can significantly improve system performance and energy efficiency. For example, comparing to Hybrid Memory Cube (HMC), MOCA can speedup application execution time by 2.6x, reduce communication latency by 75%, and improve energy efficiency by 3.4x for 256-core processors in 7 nm technology.

The power delivery system (PDS) plays a crucial role of guaranteeing the proper functionality of many-core processors. However, as PDS is usually optimized to provide power to the target chip at its best performance level, its energy efficiency can be seriously degraded under highly dynamic workloads, making it a major source of system power losses. On-chip voltage regulators (VR), which are able to achieve fast and fine-grained power control, have been popular choices for PDS implementation and provided design opportunities for improving system energy efficiency. In this paper, we propose the adaptive Quantized Power Management (QPM) scheme to dynamically adjust the PDS with both on-chip and off-chip VRs based on run-time workloads. Experimental results on different applications show that QPM applied on a hybrid PDS with both on/off-chip voltage regulators (VR) achieves 74.1% average overall energy efficiency, 12.3% higher than the conventional PDS with single off-chip VR.

The design of power delivery system plays a crucial role in guaranteeing the proper functionality of many-core processor systems. The power loss suffered on power delivery has become a salient part of total power consumption, and the energy efficiency of a highly dynamic system has been significantly challenged. Being able to achieve a fast response time and multiple voltage domain control, on-chip voltage regulators (VRs) have become popular choices to enable fine-grain power management, which also enlarge the design space of power delivery systems. This paper analytically studies different power delivery system paradigms and power management schemes in terms of energy efficiency, area overhead, and power pin occupation. The analysis shows that compared to the conventional paradigm with off-chip VRs, hybrid paradigms with both on-chip and off-chip VRs are able to maintain high efficiency in a larger range of workloads, though they suffer from low efficiency at light workload. Employed with the quantized power management scheme, the hybrid paradigm can improve the system energy efficiency at light workload by a maximum of 136% compared to the traditional load balanced scheme. Besides this, the in-package (iP) hybrid paradigm further shows its advantage in reducing the physical overheads. The results reveal that at 120 W workload, it occupies only a 10.94% total footprint area or 39.07% power pins of that of the off-chip paradigm. We conclude that the iP hybrid paradigm achieves the best tradeoffs between efficiency, physical overhead, and realization of fine-grain power management.

Recent advances in photonics technologies have made optical interconnection network an attractive option for computing systems from high-performance computers and data centers to automobiles and cellphones. Optical interconnection network promises ultra-high bandwidth, low latency, and great energy efficiency to alleviate the inter-rack, intra-rack, intraboard, and intra-chip communication bottlenecks in multiprocessor systems. Silicon-based photonics technologies piggyback onto developed silicon fabrication processes to provide viable and cost-effective solutions. Both industry and academia have invested significant efforts to develop and commercialize optical interconnection network technologies. This paper reviews the latest progresses and provides insights into the challenges and future developments.

The number of chip pins is limited due to the cost and reliability issues of sophisticated packages, and it is predicted that the chip pin count will be overstretched to satisfy the requirements of both power delivery and memory access. The gap between the achievable pin count and the demand will increase as the technology scales, due to the increasing computation resources and supply current. Pin reduction techniques are thus required for continued computing performance growth. In this article, we propose a chip pin constraint alleviation strategy, through on/off-chip power delivery system co-design, to effectively reduce the demand for power pins. An analytical model of a power delivery system, consisting of on/off-chip regulators and a power delivery network, is proposed to evaluate the influence of regulator design and package conduction loss. By combining this model with a multi-core processor model of performance and memory bandwidth requirements, we characterize the entire multi-core processor system to investigate the relationship between the chip pin constraint and performance in multi-core processor scaling and the effectiveness of our strategy. Experiments show that with the conventional power delivery system design, the chip pin constraint severely limits the performance growth as the technology scales. Using the on/off-chip power delivery system co-design, our strategy achieves a significant pin count reduction, for example, 31.3% at the 8nm technology node, compared to the conventional design with the same chip performance, while, provided with the same chip pin count, it is able to improve, by 35.0%, the chip performance at 8nm compared to the conventional design. For real applications of different parallelism, our strategy outperforms its counterpart, with a 23.7% performance improvement on average at the 8nm technology node.

Recently, interchip/intrachip optical interconnection networks have been proposed for ultrahigh-bandwidth and low-latency communications. These networks employ the microresonators (MRs) to modulate, direct, or detect the optical signal. However, utilized MRs suffer from intrinsic crosstalk noise and signal power loss, degrading the network efficiency via the signal-to-noise ratio (SNR). The amount of crosstalk noise and signal power loss may differ from network to network. Hence, there exists a need to systematically analyze the effect of the crosstalk noise and the power loss issues. In this paper, we have developed the analytical models considering both coherent and incoherent crosstalk for both the interchip and intrachip optical networks. The interchip/intrachip optical interconnection networks—the$\text{I}^{2}$CON—are analyzed as a case study. The quantitative results on the individual networks have demonstrated that the architectural design determines the impact of crosstalk on the SNR. We have also demonstrated that the optical interconnection networks with interchip/intrachip interconnects result in better bit error rate (BER) compared with that of only intrachip interconnect. Our analyses of the worst case can be utilized as a platform to compare the realistic performance among different optical interconnection networks via the degradation of SNR/BER and data bandwidth.

Power gating (PG) is one of the most effective techniques to reduce the leakage power in multiprocessor system-on-chips (MPSoCs). However, the power-mode transition during the PG period of an individual processing unit (PU) will introduce serious power/ground (P/G) noise to the neighboring PUs. As technology scales, the P/G noise problem becomes a severe reliability threat to MPSoCs. At the same time, the increasing manufacturing process variations (PVs) also bring uncertainties to the P/G noise problem and make it difficult to predict and mitigate. To tackle this problem, in this paper, we analyze the PG-induced P/G noise in the presence of PVs and propose a hardware–software collaborated runtime technique to adaptively protect PUs from P/G noise. Sensor network-on-chip is used to gather noise information and coordinate different system components. An online PV-aware algorithm is developed to effectively decide the noise impact range and arrange protections for affected PUs based on the collected noise information. We evaluate the proposed technique through cycle-level Monte Carlo simulations of NoC-based MPSoCs in different scales. The experimental results on various realistic applications show that our technique could achieve comparable reliability to the most reliable static technique while improve on average 3.78%–29.5% the system energy efficiency and reduce 15.7%–70.4% the performance penalty on different MPSoC scales.

With the fast development of inter/intra-chip optical interconnects, the gap between the data rates of electrical interconnects and optical interconnects is continuously increasing. Electrical–optical (E-O) interfaces and optical–electrical (O-E) interfaces are a pair of components that convert data between parallel electrical interconnects and serial optical interconnects. This paper holistically models and analyzes E-O and O-E interfaces in terms of energy consumption, area, and latency. Traditional interfaces, where data are converted between parallel and serial ports by serializers and deserializers (SerDes), are studied. A new type of E-O and O-E interface, which serializes and deserializes data by optical weaving technologies, are proposed alongside. Traditional interfaces will become a bottleneck for the further development of optical interconnects in the near future because of the high energy consumption and large area of SerDes necessitating new technologies. Our analysis shows that optical weaving interfaces have a better overall performance than traditional interfaces. For example, if there are 64 parallel electrical interconnects and four optical wavelengths, optical weaving interfaces can achieve a 81.6% improvement in energy consumption and a 40.8% improvement in area, compared with traditional interfaces.

With the fast development of processor chips, power-efficient, high-bandwidth, and low-latency interchip interconnects become more and more important. Studies show that the bandwidth of traditional parallel interconnects with low I/O clock frequencies will become bottlenecks in the near future. To solve this problem, two types of high-bandwidth interchip interconnects are developed. Low-swing differential electrical interconnects have widely been used in high-speed I/O designs. On the other hand, optical interconnects promise high bandwidth, low latency, and could improve the chip pin performance for manycore processors. They are becoming potential alternatives for electrical interconnects. This paper systematically models these two types of interconnects in terms of crosstalk noises, attenuation, and receiver sensitivities. Based on the proposed models, we developed optical and electrical interfaces and links (OEIL) and an analysis tool for OEIL. The OEIL can be used to analyze the energy consumption, bandwidth density, and latency of interconnects. Analytical models are verified by the results of published experiments. It shows that the optical interconnects have much higher bandwidth densities than the electrical interconnects. With this feature, the optical interconnects can significantly reduce I/O pin count compared with the electrical interconnects. For example, they can save at least 92% signal pins when connecting chips more than 25 cm (10 in) apart. The energy consumption of optical interconnects is comparable with that of electrical interconnects, and the latency of polymer waveguide-based optical interconnects is 18% less than that of electrical interconnect.

Chip I/O pins are an increasingly limited resource and significantly affect the performance, power and cost of multicore processors. Optical interconnects promise low power and high bandwidth, and are potential alternatives to electrical interconnects. This work systematically developed a set of analytical models for electrical and optical interconnects to study their structures, receiver sensitivities, crosstalk noises, and attenuations. We verified the models by published implementation results. The analytical models quantitatively identified the advantages of optical interconnects in terms of bandwidth, energy consumption, and transmission distance. We showed that optical interconnects can significantly reduce chip pin counts. For example, compared to electrical interconnects, optical interconnects can save at least 92% signal pins when connecting chips more than 25 cm (10 inches) apart.

Design of power delivery system has great influence on the power management in many-core processor systems. Moving voltage regulators from off-chip to on-chip gains more and more interest in the power delivery system design, because it is able to provide fine-grained dynamic voltage scaling. Previous works are proposed to implement power efficient on-chip voltage regulators. It is important to analyze the characteristics of the entire power delivery system to explore the tradeoff between the promising properties and costs of employing on-chip voltage regulators, especially the on-chip buck converters. In this paper, we present a novel analysis and design optimization platform of power delivery system called power supply on-chip (PowerSoC). It employs an analytical model to provide an accurate and fast evaluation of important characteristics, e.g., power efficiency, output stability, and dynamic voltage scaling, for the entire power delivery system consisting of on-chip/off-chip buck converters and power delivery network. Based on our model, geometric programming is utilized to find the optimal design for different power delivery systems and explore the tradeoff of using on-chip converters. Compared with SPICE simulations, our model achieves a simulation time reduction of six to seven orders of magnitude within 5% model error for the characteristic evaluation of different power delivery systems. By using PowerSoC, various architectures of power delivery systems are optimized for power efficiency under constraints of output stability, area, etc. Simulation results show that the hybrid architecture, consisting of both on-chip and off-chip converters, achieves 1.0% power efficiency improvement and 66.4% area reduction of converters, compared to the conventional design. We conclude the hybrid architecture has potential for efficient dynamic voltage scaling, small area, and the adaptability of the change of power delivery network parasitic, but careful account for the overhead of on-chip converters is needed.

Optical networks-on-chip (ONoCs) using wavelength-division multiplexing (WDM) technology have progressively attracted more and more attention for their use in tackling the high-power consumption and low bandwidth issues in growing metallic interconnection networks in multiprocessor systems-on-chip. However, the basic optical devices employed to construct WDM-based ONoCs are imperfect and suffer from inevitable power loss and crosstalk noise. Furthermore, when employing WDM, optical signals of various wavelengths can interfere with each other through different optical switching elements within the network, creating crosstalk noise. As a result, the crosstalk noise in large-scale WDM-based ONoCs accumulates and causes severe performance degradation, restricts the network scalability, and considerably attenuates the signal-to-noise ratio (SNR). In this paper, we systematically study and compare the worst case as well as the average crosstalk noise and SNR in three well-known optical interconnect architectures, mesh-based, folded-torus-based, and fat-tree-based ONoCs using WDM. The analytical models for the worst case and the average crosstalk noise and SNR in the different architectures are presented. Furthermore, the proposed analytical models are integrated into a newly developed crosstalk noise and loss analysis platform (CLAP) to analyze the crosstalk noise and SNR in WDM-based ONoCs of any network size using an arbitrary optical router. Utilizing CLAP, we compare the worst case as well as the average crosstalk noise and SNR in different WDM-based ONoC architectures. Furthermore, we indicate how the SNR changes in respect to variations in the number of optical wavelengths in use, the free-spectral range, and the microresonators$\boldsymbol {Q}$factor. The analyses’ results demonstrate that the crosstalk noise is of critical concern to WDM-based ONoCs: in the worst case, the crosstalk noise power exceeds the signal power in all three WDM-based ONoC architectures, even when the number of processor cores is small, e.g., 64.

Basic photonic devices in inter- and intra-chip optical networks suffer from inevitable power loss and crosstalk noise. Incoherent crosstalk introduces quick power fluctuations, while coherent crosstalk varies the optical power of the optical signal in optical interconnection networks (OINs). As a result, the accumulative crosstalk in large scale OINs considerably hurts the signal-to-noise ratio (SNR) and imposes high power penalties. In this work, we aim at studying the worst-case incoherent and coherent crosstalk in OINs at the system level. The proposed analytical models are integrated into a newly developed crosstalk and loss analysis platform, called CLAP, to facilitate the SNR analyses in arbitrary OINs.

Multiprocessor systems-on-chip show a trend toward integration of tens and hundreds of processor cores on a single chip. With the development of silicon photonics for short-haul optical communication, wavelength division multiplexing (WDM)-based optical networks-on-chip (ONoCs) are emerging on-chip communication architectures that can potentially offer high bandwidth and power efficiency. Thermal sensitivity of photonic devices is one of the main concerns about the on-chip optical interconnects. We systematically modeled thermal effects in optical links in WDM-based ONoCs. Based on the proposed thermal models, we developed OTemp, an optical thermal effect modeling platform for optical links in both WDM-based ONoCs and single-wavelength ONoCs. OTemp can be used to simulate the power consumption as well as optical power loss for optical links under temperature variations. We use case studies to quantitatively analyze the worst-case power consumption for one wavelength in an eight-wavelength WDM-based optical link under different configurations of low-temperature-dependence techniques. Results show that the worst-case power consumption increases dramatically with on-chip temperature variations. Thermal-based adjustment and optimal device settings can help reduce power consumption under temperature variations. Assume that off-chip vertical-cavity surface-emitting lasers are used as the laser source with WDM channel spacing of 1 nm, if we use thermal-based adjustment with guard rings for channel remapping, the worst-case total power consumption is 6.7 pJ/bit under the maximum temperature variation of 60 °C; larger channel spacing would result in a larger worst-case power consumption in this case. If we use thermal-based adjustment without channel remapping, the worst-case total power consumption is around 9.8 pJ/bit under the maximum temperature variation of 60 °C; in this case, the worst-case power consumption would benefit from a larger channel spacing.