Design Methodologies and Architecture Solutions for High-Performance Interconnects

ABSTRACT In Deep Sub-Micron (DSM) technologies, interconnects play a crucial role in the correct functionality and largely impact the performance of complex System-on-Chip (SoC) designs. For technologies of 0.25µm and below, wiring capacitance dominates gate capacitance, thus rapidly increasing the interconnect-induced delay. Moreover, the coupling capacitance becomes a significant portion of the on-chip total wiring capacitance, and coupling between adjacent wires cannot be considered as a second-order effect any longer. As a consequence, the traditional top-down design methodology is ineffective, since the actual wiring delays can be computed only after layout parasitic extraction, when the physical design is completed. Fixing all the timing violations often requires several time-consuming iterations of logical and physical design, and it is essentially a trial-and-error approach. Increasingly tighter time-to-market requirements dictate that interconnect parasitics must be taken into account during all phases of the design flow, at different level of abstractions. However, given the aggressive technology scaling trends and the growing design complexity, this approach will only temporarily ameliorate the interconnect problem. We believe that in order to achieve gigascale designs in the nanometer regime, a novel design paradigm, based on new forms of regularity and newly created IP (Intellectual Property) blocks must be developed, to provide a direct path from system-level architectural exploration to physical implementation

A 90 nm CMOS 16 Gb/s Transceiver for Optical Interconnects

Interconnect architectures which leverage high-bandwidth optical channels offer a promising solution to address the increasing chip-to-chip I/O bandwidth demands. This paper describes a dense, high-speed, and low-power CMOS optical interconnect transceiver architecture. Vertical-cavity surface-emitting laser (VCSEL) data rate is extended for a given average current and corresponding reliability level with a four-tap current summing FIR transmitter. A low-voltage integrating and double-sampling optical receiver front-end provides adequate sensitivity in a power efficient manner by avoiding linear high-gain elements common in conventional transimpedance-amplifier (TIA) receivers. Clock recovery is performed with a dual-loop architecture which employs baud-rate phase detection and feedback interpolation to achieve reduced power consumption, while high-precision phase spacing is ensured at both the transmitter and receiver through adjustable delay clock buffers. A prototype chip fabricated in 1 V 90 nm CMOS achieves 16 Gb/s operation while consuming 129 mW and occupying 0.105 mm^2

On-Chip Transparent Wire Pipelining (invited paper)

Wire pipelining has been proposed as a viable mean to break the discrepancy between decreasing gate delays and increasing wire delays in deep-submicron technologies. Far from being a straightforwardly applicable technique, this methodology requires a number of design modifications in order to insert it seamlessly in the current design flow. In this paper we briefly survey the methods presented by other researchers in the field and then we thoroughly analyze the solutions we recently proposed, ranging from system-level wire pipelining to physical design aspects

On-Disk Data Processing: Issues and Future Directions

In this paper, we present a survey of "on-disk" data processing (ODDP). ODDP, which is a form of near-data processing, refers to the computing arrangement where the secondary storage drives have the data processing capability. Proposed ODDP schemes vary widely in terms of the data processing capability, target applications, architecture and the kind of storage drive employed. Some ODDP schemes provide only a specific but heavily used operation like sort whereas some provide a full range of operations. Recently, with the advent of Solid State Drives, powerful and extensive ODDP solutions have been proposed. In this paper, we present a thorough review of architectures developed for different on-disk processing approaches along with current and future challenges and also identify the future directions which ODDP can take.Comment: 24 pages, 17 Figures, 3 Table

A Micro Power Hardware Fabric for Embedded Computing

Field Programmable Gate Arrays (FPGAs) mitigate many of the problemsencountered with the development of ASICs by offering flexibility, faster time-to-market, and amortized NRE costs, among other benefits. While FPGAs are increasingly being used for complex computational applications such as signal and image processing, networking, and cryptology, they are far from ideal for these tasks due to relatively high power consumption and silicon usage overheads compared to direct ASIC implementation. A reconfigurable device that exhibits ASIC-like power characteristics and FPGA-like costs and tool support is desirable to fill this void. In this research, a parameterized, reconfigurable fabric model named as domain specific fabric (DSF) is developed that exhibits ASIC-like power characteristics for Digital Signal Processing (DSP) style applications. Using this model, the impact of varying different design parameters on power and performance has been studied. Different optimization techniques like local search and simulated annealing are used to determine the appropriate interconnect for a specific set of applications. A design space exploration tool has been developed to automate and generate a tailored architectural instance of the fabric.The fabric has been synthesized on 160 nm cell-based ASIC fabrication process from OKI and 130 nm from IBM. A detailed power-performance analysis has been completed using signal and image processing benchmarks from the MediaBench benchmark suite and elsewhere with comparisons to other hardware and software implementations. The optimized fabric implemented using the 130 nm process yields energy within 3X of a direct ASIC implementation, 330X better than a Virtex-II Pro FPGA and 2016X better than an Intel XScale processor