Continued improvement in computing efficiency requires functional specialization of hardware designs. Agile hardware design methodologies have been proposed to alleviate the increased design costs of custom silicon architectures, but their practice thus far has been accompanied with challenges in integration and validation of complex systems-on-a-chip. We present the Chipyard framework, an integrated SoC design, simulation, and implementation environment for specialized compute systems. Chipyard includes configurable, composable, open-source, generator-based IP blocks that can be used across multiple stages of the hardware development flow while maintaining design intent and integration consistency. Through cloud-hosted FPGA-accelerated simulation and rapid ASIC implementation, Chipyard enables continuous validation of physically realizable customized systems.

Design and experimental evaluation of a new sense-amplifier-based flip-flop (SAFF) is presented. It was found that the main speed bottleneck of existing SAFF's is the cross-coupled set-reset (SR) latch in the output stage. The new flip-flop uses a new output stage latch topology that significantly reduces delay and improves driving capability. The performance of this flip-flop is verified by measurements on a test chip implemented in 0.18 /spl mu/m effective channel length CMOS. Demonstrated speed places it among the fastest flip-flops used in the state-of-the-art processors. Measurement techniques employed in this work as well as the measurement set-up are discussed in this paper.

We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation. Unlike prior FPGA-accelerated simulation tools, FireSim runs on Amazon EC2 F1, a public cloud FPGA platform, which greatly improves usability, provides elasticity, and lowers the cost of large-scale FPGA-based experiments. We describe the design and implementation of FireSim and show how it can provide sufficient performance to run modern applications at scale, to enable true hardware-software co-design. As an example, we demonstrate automatically generating and deploying a target cluster of 1,024 3.2 GHz quad-core server nodes, each with 16 GB of DRAM, interconnected by a 200 Gbit/s network with 2 microsecond latency, which simulates at a 3.4 MHz processor clock rate (less than 1,000x slowdown over real-time). In aggregate, this FireSim instantiation simulates 4,096 cores and 16 TB of memory, runs ^~ 14 billion instructions per second, and harnesses 12.8 million dollars worth of FPGAs-at a total cost of only ^~ $100 per simulation hour to the user. We present several examples to show how FireSim can be used to explore various research directions in warehouse-scale machine design, including modeling networks with high-bandwidth and low-latency, integrating arbitrary RTL designs for a variety of commodity and specialized datacenter nodes, and modeling a variety of datacenter organizations, as well as reusing the scale-out FireSim infrastructure to enable fast, massively parallel cycle-exact single-node microarchitectural experimentation.

The class of low-density parity-check (LDPC) codes is attractive, since such codes can be decoded using practical message-passing algorithms, and their performance is known to approach the Shannon limits for suitably large block lengths. For the intermediate block lengths relevant in applications, however, many LDPC codes exhibit a so-called ¿error floor,¿ corresponding to a significant flattening in the curve that relates signal-to-noise ratio (SNR) to the bit-error rate (BER) level. Previous work has linked this behavior to combinatorial substructures within the Tanner graph associated with an LDPC code, known as (fully) absorbing sets. These fully absorbing sets correspond to a particular type of near-codewords or trapping sets that are stable under bit-flipping operations, and exert the dominant effect on the low BER behavior of structured LDPC codes. This paper provides a detailed theoretical analysis of these (fully) absorbing sets for the class of Cp, ¿ array-based LDPC codes, including the characterization of all minimal (fully) absorbing sets for the array-based LDPC codes for ¿ = 2,3,4, and moreover, it provides the development of techniques to enumerate them exactly. Theoretical results of this type provide a foundation for predicting and extrapolating the error floor behavior of LDPC codes.

A 1.8-V 14-b 12-MS/s pseudo-differential pipeline analog-to-digital converter (ADC) using a passive capacitor error-averaging technique and a nested CMOS gain-boosting technique is described. The converter is optimized for low-voltage low-power applications by applying an optimum stage-scaling algorithm at the architectural level and an opamp and comparator sharing technique at the circuit level. Prototyped in a 0.18-/spl mu/m 6M-1P CMOS process, this converter achieves a peak signal-to-noise plus distortion ratio (SNDR) of 75.5 dB and a 103-dB spurious-free dynamic range (SFDR) without trimming, calibration, or dithering. With a 1-MHz analog input, the maximum differential nonlinearity is 0.47 LSB and the maximum integral nonlinearity is 0.54 LSB. The large analog bandwidth of the front-end sample-and-hold circuit is achieved using bootstrapped thin-oxide transistors as switches, resulting in an SFDR of 97 dB when a 40-MHz full-scale input is digitized. The ADC occupies an active area of 10 mm/sup 2/ and dissipates 98 mW.

We present an adaptive digital technique to calibrate pipelined analog-to-digital converters (ADCs). Rather than achieving linearity by adjustment of analog component values, the new approach infers component errors from conversion results and applies digital postprocessing to correct those results. The scheme proposed here draws close analogy to the channel equalization problem commonly encountered in digital communications. We show that, with the help of a slow but accurate ADC, the proposed code-domain adaptive finite-impulse-response filter is sufficient to remove the effect of component errors including capacitor mismatch, finite op-amp gain, op-amp offset, and sampling-switch-induced offset, provided they are not signal-dependent. The algorithm is all digital, fully adaptive, data-driven, and operates in the background. Strong tradeoffs between accuracy and speed of pipelined ADCs are greatly relaxed in this approach with the aid of digital correction techniques. Analog precision problems are translated into the complexity of digital signal-processing circuits, allowing this approach to benefit from CMOS device scaling in contrast to most conventional correction techniques.