1,087 research outputs found
Exploration of photonic networks on chip with the University of Ferrara
Modern chips include several processors that communicate through an interconnection network, which has a direct impact on system performance, power consumption and chip area. Recent research points out the great potential of optical networks to reduce on-chip communication latency and energy. We plan to explore this state-of-the-art field during an internship in the University of Ferrara
Recommended from our members
Cross-Layer Pathfinding for Off-Chip Interconnects
Off-chip interconnects for integrated circuits (ICs) today induce a diverse design space, spanning many different applications that require transmission of data at various bandwidths, latencies and link lengths. Off-chip interconnect design solutions are also variously sensitive to system performance, power and cost metrics, while also having a strong impact on these metrics. The costs associated with off-chip interconnects include die area, package (PKG) and printed circuit board (PCB) area, technology and bill of materials (BOM). Choices made regarding off-chip interconnects are fundamental to product definition, architecture, design implementation and technology enablement. Given their cross-layer impact, it is imperative that a cross-layer approach be employed to architect and analyze off-chip interconnects up front, so that a top-down design flow can comprehend the cross-layer impacts and correctly assess the system performance, power and cost tradeoffs for off-chip interconnects. Chip architects are not exposed to all the tradeoffs at the physical and circuit implementation or technology layers, and often lack the tools to accurately assess off-chip interconnects. Furthermore, the collaterals needed for a detailed analysis are often lacking when the chip is architected; these include circuit design and layout, PKG and PCB layout, and physical floorplan and implementation. To address the need for a framework that enables architects to assess the system-level impact of off-chip interconnects, this thesis presents power-area-timing (PAT) models for off-chip interconnects, optimization and planning tools with the appropriate abstraction using these PAT models, and die/PKG/PCB co-design methods that help expose the off-chip interconnect cross-layer metrics to the die/PKG/PCB design flows. Together, these models, tools and methods enable cross-layer optimization that allows for a top-down definition and exploration of the design space and helps converge on the correct off-chip interconnect implementation and technology choice. The tools presented cover off-chip memory interfaces for mobile and server products, silicon photonic interfaces, 2.5D silicon interposers and 3D through-silicon vias (TSVs). The goal of the cross-layer framework is to assess the key metrics of the interconnect (such as timing, latency, active/idle/sleep power, and area/cost) at an appropriate level of abstraction by being able to do this across layers of the design flow. In additional to signal interconnect, this thesis also explores the need for such cross-layer pathfinding for power distribution networks (PDN), where the system-on-chip (SoC) floorplan and pinmap must be optimized before the collateral layouts for PDN analysis are ready. Altogether, the developed cross-layer pathfinding methodology for off-chip interconnects enables more rapid and thorough exploration of a vast design space of off-chip parallel and serial links, inter-die and inter-chiplet links and silicon photonics. Such exploration will pave the way for off-chip interconnect technology enablement that is optimized for system needs. The basis of the framework can be extended to cover other interconnect technology as well, since it fundamentally relates to system-level metrics that are common to all off-chip interconnects
Resource and thermal management in 3D-stacked multi-/many-core systems
Continuous semiconductor technology scaling and the rapid increase in computational needs have stimulated the emergence of multi-/many-core processors. While up to hundreds of cores can be placed on a single chip, the performance capacity of the cores cannot be fully exploited due to high latencies of interconnects and memory, high power consumption, and low manufacturing yield in traditional (2D) chips. 3D stacking is an emerging technology that aims to overcome these limitations of 2D designs by stacking processor dies over each other and using through-silicon-vias (TSVs) for on-chip communication, and thus, provides a large amount of on-chip resources and shortens communication latency. These benefits, however, are limited by challenges in high power densities and temperatures.
3D stacking also enables integrating heterogeneous technologies into a single chip. One example of heterogeneous integration is building many-core systems with silicon-photonic network-on-chip (PNoC), which reduces on-chip communication latency significantly and provides higher bandwidth compared to electrical links. However, silicon-photonic links are vulnerable to on-chip thermal and process variations. These variations can be countered by actively tuning the temperatures of optical devices through micro-heaters, but at the cost of substantial power overhead.
This thesis claims that unearthing the energy efficiency potential of 3D-stacked systems requires intelligent and application-aware resource management. Specifically, the thesis improves energy efficiency of 3D-stacked systems via three major components of computing systems: cache, memory, and on-chip communication. We analyze characteristics of workloads in computation, memory usage, and communication, and present techniques that leverage these characteristics for energy-efficient computing.
This thesis introduces 3D cache resource pooling, a cache design that allows for flexible heterogeneity in cache configuration across a 3D-stacked system and improves cache utilization and system energy efficiency. We also demonstrate the impact of resource pooling on a real prototype 3D system with scratchpad memory.
At the main memory level, we claim that utilizing heterogeneous memory modules and memory object level management significantly helps with energy efficiency. This thesis proposes a memory management scheme at a finer granularity: memory object level, and a page allocation policy to leverage the heterogeneity of available memory modules and cater to the diverse memory requirements of workloads.
On the on-chip communication side, we introduce an approach to limit the power overhead of PNoC in (3D) many-core systems through cross-layer thermal management. Our proposed thermally-aware workload allocation policies coupled with an adaptive thermal tuning policy minimize the required thermal tuning power for PNoC, and in this way, help broader integration of PNoC. The thesis also introduces techniques in placement and floorplanning of optical devices to reduce optical loss and, thus, laser source power consumption.2018-03-09T00:00:00
Towards Compelling Cases for the Viability of Silicon-Nanophotonic Technology in Future Many-core Systems
Many crossbenchmarking results reported in the open literature raise optimistic expectations on the use of optical networks-on-chip (ONoCs) for high-performance and low-power on-chip communications in future Manycore Systems. However, these works ultimately fail to make a compelling case for the viability of silicon-nanophotonic technology for two fundamental reasons:
(1)Lack of aggressive electrical baselines (ENoCs).
(2) Inaccuracy in physical- and architecture-layer analysis of the ONoC.
This thesis aims at providing the guidelines and minimum requirements so that nanophotonic emerging technology may become of practical relevance. The key enabler for this study is a cross-layer design methodology of the optical transport medium, ranging from the consideration of the predictability gap between ONoC logic schemes and their physical implementations, up to architecture-level design issues such as the network interface and its co-design requirements with the memory hierarchy. In order to increase the practical relevance of the study, we consider a consolidated electrical NoC counterpart with an optimized architecture from a performance and power viewpoint. The quality metrics of this latter are derived from synthesis and place&route on an industrial 40nm low-power technology library. Building on this methodology, we are able to provide a realistic energy efficiency comparison between ONoC and ENoC both at the level of the system interconnect and of the system as a whole, pointing out the sensitivity of the results to the maturity of the underlying silicon nanophotonic technology, and at the same time paving the way towards compelling cases for the viability of such technology in next generation many-cores systems
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
Global Congestion and Fault Aware Wireless Interconnection Framework for Multicore Systems
Multicore processors are getting more common in the implementation of all type of computing demands, starting from personal computers to the large server farms for high computational demanding applications. The network-on-chip provides a better alternative to the traditional bus based communication infrastructure for this multicore system. Conventional wire-based NoC interconnect faces constraints due to their long multi-hop latency and high power consumption. Furthermore high traffic generating applications sometimes creates congestion in such system further degrading the systems performance. In this thesis work, a novel two-state congestion aware wireless interconnection framework for network chip is presented. This WiNoC system was designed to able to dynamically redirect traffic to avoid congestion based on network condition information shared among all the core tiles in the system. Hence a novel routing scheme and a two-state MAC protocol is proposed based on a proposed two layer hybrid mesh-based NoC architecture. The underlying mesh network is connected via wired-based interconnect and on top of that a shared wireless interconnect framework is added for single-hop communication. The routing scheme is non-deterministic in nature and utilizes the principles from existing dynamic routing algorithms. The MAC protocol for the wireless interface works in two modes. The first is data mode where a token-based protocol is utilized to transfer core data. And the second mode is the control mode where a broadcast-based communication protocol is used to share the network congestion information. The work details the switching methodology between these two modes and also explain, how the routing scheme utilizes the congestion information (gathered during the control mode) to route data packets during normal operation mode. The proposed work was modeled in a cycle accurate network simulator and its performance were evaluated against traditional NoC and WiNoC designs
Robust and Traffic Aware Medium Access Control Mechanisms for Energy-Efficient mm-Wave Wireless Network-on-Chip Architectures
To cater to the performance/watt needs, processors with multiple processing cores on the same chip have become the de-facto design choice. In such multicore systems, Network-on-Chip (NoC) serves as a communication infrastructure for data transfer among the cores on the chip. However, conventional metallic interconnect based NoCs are constrained by their long multi-hop latencies and high power consumption, limiting the performance gain in these systems. Among, different alternatives, due to the CMOS compatibility and energy-efficiency, low-latency wireless interconnect operating in the millimeter wave (mm-wave) band is nearer term solution to this multi-hop communication problem. This has led to the recent exploration of millimeter-wave (mm-wave) wireless technologies in wireless NoC architectures (WiNoC).
To realize the mm-wave wireless interconnect in a WiNoC, a wireless interface (WI) equipped with on-chip antenna and transceiver circuit operating at 60GHz frequency range is integrated to the ports of some NoC switches. The WIs are also equipped with a medium access control (MAC) mechanism that ensures a collision free and energy-efficient communication among the WIs located at different parts on the chip. However, due to shrinking feature size and complex integration in CMOS technology, high-density chips like multicore systems are prone to manufacturing defects and dynamic faults during chip operation. Such failures can result in permanently broken wireless links or cause the MAC to malfunction in a WiNoC. Consequently, the energy-efficient communication through the wireless medium will be compromised. Furthermore, the energy efficiency in the wireless channel access is also dependent on the traffic pattern of the applications running on the multicore systems. Due to the bursty and self-similar nature of the NoC traffic patterns, the traffic demand of the WIs can vary both spatially and temporally. Ineffective management of such traffic variation of the WIs, limits the performance and energy benefits of the novel mm-wave interconnect technology. Hence, to utilize the full potential of the novel mm-wave interconnect technology in WiNoCs, design of a simple, fair, robust, and efficient MAC is of paramount importance.
The main goal of this dissertation is to propose the design principles for robust and traffic-aware MAC mechanisms to provide high bandwidth, low latency, and energy-efficient data communication in mm-wave WiNoCs. The proposed solution has two parts. In the first part, we propose the cross-layer design methodology of robust WiNoC architecture that can minimize the effect of permanent failure of the wireless links and recover from transient failures caused by single event upsets (SEU). Then, in the second part, we present a traffic-aware MAC mechanism that can adjust the transmission slots of the WIs based on the traffic demand of the WIs. The proposed MAC is also robust against the failure of the wireless access mechanism. Finally, as future research directions, this idea of traffic awareness is extended throughout the whole NoC by enabling adaptiveness in both wired and wireless interconnection fabric
Neural networks-on-chip for hybrid bio-electronic systems
PhD ThesisBy modelling the brains computation we can further our understanding
of its function and develop novel treatments for neurological disorders. The
brain is incredibly powerful and energy e cient, but its computation does
not t well with the traditional computer architecture developed over the
previous 70 years. Therefore, there is growing research focus in developing
alternative computing technologies to enhance our neural modelling capability,
with the expectation that the technology in itself will also bene t from
increased awareness of neural computational paradigms.
This thesis focuses upon developing a methodology to study the design
of neural computing systems, with an emphasis on studying systems suitable
for biomedical experiments. The methodology allows for the design to be
optimized according to the application. For example, di erent case studies
highlight how to reduce energy consumption, reduce silicon area, or to
increase network throughput.
High performance processing cores are presented for both Hodgkin-Huxley
and Izhikevich neurons incorporating novel design features. Further, a complete
energy/area model for a neural-network-on-chip is derived, which is
used in two exemplar case-studies: a cortical neural circuit to benchmark
typical system performance, illustrating how a 65,000 neuron network could
be processed in real-time within a 100mW power budget; and a scalable highperformance
processing platform for a cerebellar neural prosthesis. From
these case-studies, the contribution of network granularity towards optimal
neural-network-on-chip performance is explored
Recommended from our members
Hardware-Software Integrated Silicon Photonic Systems
Fabrication of integrated photonic devices and circuits in a CMOS-compatible process or foundry is the essence of the silicon photonic platform. Optical devices in this platform are enabled by the high index contrast between silicon and silicon on insulator. These devices offer potential benefits when integrated with existing and emerging high performance microelectronics. Integration of silicon photonics with small footprints and power-efficient and high-bandwidth operation has long been cited as a solution to existing issues in high performance interconnects for telecommunications and data communication. Stemming from this historic application in communications, new applications in sensing arrays, biochemistry, and even entertainment continue to grow. However, for many technologies to successfully adopt silicon photonics and reap the perceived benefits, the silicon photonic platform must extend toward development of a full ecosystem. Such extension includes implementation of low cost and robust electronic-photonic packaging techniques for all applications. In an ecosystem implemented with services ranging from device fabrication all the way to packaged products, ease-of-use and ease-of-deployment in systems that require many hardware and software components becomes possible.
With the onset of the Internet of Things (IoT), nearly all technologies—sensors, compute, communication devices, etc.—persist in systems with some level of localized or distributed software interaction. These interactions often require a level of networked communications. For silicon photonics to penetrate technologies comprising IoT, it is advantageous to implement such devices in a hardware-software integrated way. Meaning, all functionalities and interactions related to the silicon photonic devices are well defined in terms of the physicality of the hardware. This hardware is then abstracted into various levels of software as needed in the system. The power of hardware-software integration allows many of the piece-wise demonstrated functionalities of silicon photonics to easily translate to commercial implementation.
This work begins by briefly highlighting the challenges and solutions for transforming existing silicon photonic platforms to a full-fledged silicon photonic ecosystem. The highlighted solutions in development consist of tools for fabrication, testing, subsystem packaging, and system validation. Building off the knowledge of a silicon photonic ecosystem in development, this work continues by demonstrating various levels of hardware-software integration. These are primarily focused on silicon photonic interconnects.
The first hardware-software integration-focused portion of this work explores silicon microring-based devices as a key building block for greater silicon photonic subsystems. The microring’s sensitivity to thermal fluctuations is identified not as a flaw, but as a tool for functionalization. A logical control system is implemented to mitigate thermal effects that would normally render a microring resonator inoperable. The mechanism to control the microring is extended and abstracted with software programmability to offer wavelength routing as a network primitive. This functionality, available through hardware-software integration, offers the possibility for ubiquitous deployment of such microring devices in future photonic interconnection networks.
The second hardware-software integration-focused portion of this work explores dynamic silicon photonic switching devices and circuits. Specifically, interactions with and implications of high-speed data propagation and link layer control are demonstrated. The characteristics of photonic link setup include transients due to physical layer optical effects, latencies involved with initializing burst mode links, and optical link quality. The impacts on the functionalities and performance offered by photonic devices are explored. An optical network interface platform is devised using FPGAs to encapsulate hardware and software for controlling these characteristics using custom hardware description language, firmware, and software. A basic version of a silicon photonic network controller using FPGAs is used as a tool to demonstrate a highly scalable switch architecture using microring resonators. This architecture would not be possible without some semblance of this controller, combined with advanced electronic-photonic packaging. A more advanced deployment of the network interface platform is used to demonstrate a method for accelerating photonic links using out-of-band arbitration. A first demonstration of this platform is performed on a silicon photonic microring router network. A second demonstration is used to further explore the feasibility of full hardware-software integrated photonic device actuation, link layer control, and out-of-band arbitration. The demonstration is performed on a complete silicon photonic network with both spatial switching and wavelength routing functionalities.
The aforementioned hardware-software integration mechanisms are rigorously tested for data communications applications. Capabilities are shown for very reliable, low latency, and dynamic high-speed data delivery using silicon photonic devices. Applying these mechanisms to complete electronic-photonic packaged subsystems provides a strong path to commercial manifestations of functional silicon photonic devices
Evolution of Publications, Subjects, and Co-authorships in Network-On-Chip Research From a Complex Network Perspective
The academia and industry have been pursuing network-on-chip (NoC) related research since two decades ago when there was an urgency to respond to the scaling and technological challenges imposed on intra-chip communication in SoC designs. Like any other research topic, NoC inevitably goes through its life cycle: A. it started up (2000-2007) and quickly gained traction in its own right; B. it then entered the phase of growth and shakeout (2008-2013) with the research outcomes peaked in 2010 and remained high for another four/five years; C. NoC research was considered mature and stable (2014-2020), with signs showing a steady slowdown. Although from time to time, excellent survey articles on different subjects/aspects of NoC appeared in the open literature, yet there is no general consensus on where we are in this NoC roadmap and where we are heading, largely due to lack of an overarching methodology and tool to assess and quantify the research outcomes and evolution. In this paper, we address this issue from the perspective of three specific complex networks, namely the citation network, the subject citation network, and the co-authorship network. The network structure parameters (e.g., modularity, diameter, etc.) and graph dynamics of the three networks are extracted and analyzed, which helps reveal and explain the reasons and the driving forces behind all the changes observed in NoC research over 20 years. Additional analyses are performed in this study to link interesting phenomena surrounding the NoC area. They include: (1) relationships between communities in citation networks and NoC subjects, (2) measure and visualization of a subject\u27s influence score and its evolution, (3) knowledge flow among the six most popular NoC subjects and their relationships, (4) evolution of various subjects in terms of number of publications, (5) collaboration patterns and cross-community collaboration among the authors in NoC research, (6) interesting observation of career lifetime and productivity among NoC researchers, and finally (7) investigation of whether or not new authors are chasing hot subjects in NoC. All these analyses have led to a prediction of publications, subjects, and co-authorship in NoC research in the near future, which is also presented in the paper
- …