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

CMOS transceiver with baud rate clock recovery for optical interconnects

An efficient baud rate clock and data recovery architecture is applied to a double sampling/integrating front-end receiver for optical interconnects. Receiver performance is analyzed and projected for future technologies. This front-end allows use of a 1:5 demux architecture to achieve 5Gb/s in a 0.25 μm CMOS process. A 5:1 multiplexing transmitter is used to drive VCSELs for optical transmission. The transceiver chip consumes 145mW per link at 5Gb/s with a 2.5V supply

A 1.6 Gb/s, 3 mW CMOS receiver for optical communication

A 1.6 Gb/s receiver for optical communication has been designed and fabricated in a 0.25-μm CMOS process. This receiver has no transimpedance amplifier and uses the parasitic capacitor of the flip-chip bonded photodetector as an integrating element and resolves the data with a double-sampling technique. A simple feedback loop adjusts a bias current to the average optical signal, which essentially "AC couples" the input. The resulting receiver resolves an 11 μA input, dissipates 3 mW of power, occupies 80 μm x 50 μm of area and operates at over 1.6 Gb/s

Are we repeating mistakes of the past? A review of the evidence for esketamine – CORRIGENDUM

It should be emphasised that Table 1 in the Analysis1 combines data from Phase 2 trials (including open-label and placebo-controlled trial periods) and Phase 3 trials (placebo-controlled efficacy studies, a maintenance of effect study and a long-term open label safety study) submitted by Janssen to the FDA (as indicated by superscript 'b' in the Table), therefore the data does not represent a randomised comparison, and definitive causal inferences cannot be drawn. As indicated by the denominators for each entry there were more patients exposed to esketamine than placebo (and for longer periods) which may be one reason for the higher numbers of suicides in the esketamine group. However, it is worth noting other data that suggest that increased suicidality may be a feature of esketamine treatment. In the long-term safety study 14.5% of patients on esketamine (in a population selected for their lack of active suicidality) reported 'treatment-emergent' suicidal ideation (114/784), 6 patients attempted suicide in addition to the one completed suicide.2 Furthermore, a recent analysis of post-marketing surveillance data reported to the FDA for the 12 months since esketamine was licensed in the US there have already been 64 reports of suicidal ideation, 11 completed suicides, and 6 attempted suicides attributed to esketamine.3 This represents a 24-fold increased risk of report for suicidal ideation for esketamine compared with other drugs, and a 6-fold increased risk for completed suicide.3 The authors of this paper concluded that the safety of esketamine required "urgent clarification."3 It should also be clarified that although ketamine is not used routinely as an anaesthetic agent in high-income countries, like the UK, because of its unfavourable balance of risks and harms, it is used more frequently in low-income countries

Anthropology’s Science Wars Insights from a New Survey

In recent decades the field of anthropology has been characterized as sharply divided between proscience and antiscience factions. The aim of this study is to empirically evaluate that characterization. We survey anthropologists in graduate programs in the United States regarding their views of science and advocacy, moral and epistemic relativism, and the merits of evolutionary biological explanations. We examine anthropologists’ views in concert with their varying appraisals of major controversies in the discipline (Chagnon/Tierney, Mead/Freeman, and Menchú/Stoll). We find that disciplinary specialization and especially gender and political orientation are significant predictors of anthropologists’ views. We interpret our findings through the lens of an intuitionist social psychology that helps explain the dynamics of such controversies as well as ongoing ideological divisions in the field

Is the chemical imbalance an ‘urban legend’? An exploration of the status of the serotonin theory of depression in the academic literature

The theory that depression is caused by a serotonin abnormality or other chemical imbalance has become widely accepted by the public and is one prominent justification for the use of antidepressants. However, it has been increasingly questioned and there is little evidence it has empirical support. In response, leading psychiatrists suggested it was an ‘urban legend’ that was never taken seriously by the psychiatric profession. To interrogate these claims, we examined the coverage of the serotonin theory of depression in a sample of highly cited and influential academic literature from 1990, when the theory started to be popularized, to 2010 when these responses were articulated. We analysed 30 highly cited reviews of the aetiology of depression in general, 30 highly cited papers on depression and serotonin specifically and a sample of influential textbooks. The majority of the aetiology reviews supported the hypothesis, including some that were entirely devoted to describing research on the serotonin system, and those that reviewed the aetiology of depression more broadly. Research papers on the serotonin system in depression were highly cited and most of them strongly supported the serotonin theory. All textbooks supported the theory, at least in some sections, and devoted substantial coverage to it, although some also acknowledged it remained provisional. The findings suggest that the serotonin theory was endorsed by the professional and academic community. The theory is compared to an exhausted Kuhnian paradigm with professional equivocation about it acting as a means of defending it against encroaching criticism. The analysis suggests that, despite protestations to the contrary, the profession bears some responsibility for the propagation of a theory that has little empirical support and the mass antidepressant prescribing it has inspired

EIE: Efficient Inference Engine on Compressed Deep Neural Network

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x; Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly: https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision: http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University: https://goo.gl/6lwuer. Published as a conference paper in ISCA 201