Comparing Composite vs. Wave-Cores in a Novel Dark-Silicon Methodology

As transistor scaling continues to push us into new design spaces, where power density is increasingly a major performance constraint, there have been moves to explore solutions which exploit so-called Dark Silicon, the UCSD Greendroid project being a notable exemplar. In this paper, we explore one novel dark silicon methodology, based on a heterogeneous multi-accelerator system model and an implicit execution model for the host processor. We also highlight a back- end translation methodology from raw machine code into data- flow style hardware cores, and introduce two distinct implementation styles. We then demonstrate comparative power benefits as compared to a relevant CPU model, assuming a 65nm benchmark technology node for both cases

Performance Analysis of a 3D Wireless Massively Parallel Computer

In previous work, the authors presented a 3D hexagonal wireless direct-interconnect network for a massively parallel computer, with a focus on analysing processor utilisation. In this study, we consider the characteristics of such an architecture in terms of link utilisation and power consumption. We have applied a store-and-forward packet-switching algorithm to both our proposed architecture and a traditional wired 5D direct network (the same as IBM’s Blue Gene). Simulations show that for small and medium-size networks the link utility of the proposed architecture is comparable with (and in some cases even better than) traditional 5D networks. This work demonstrates that there is a potential for wireless processing array concepts to address High-Performance Computing (HPC) challenges whilst alleviating some significant physical construction drawbacks of traditional systems

A 65nm CMOS lossless bio-signal compression circuit with 250 femtoJoule performance per bit.

A 65nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel `xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the `simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250fJ/bit at continuous conversion data-rates of 5MSa/sec. This is achieved with a diminutive circuit area of 155 um2. Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization

Miniature Untethered EEG Recorder Improves Advanced Neuroscience Methodologies

Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model. Epochs capturing specific behaviors were automatically identified and extracted prior to further analysis. This was achieved by the novel design of a ultra- miniature wearable EEG recorder, the NAT-1 device, and its in- situ IR recording module. The device is described in detail, and its contribution to enabling new neuroscience is demonstrated

The evolution of lung cancer and impact of subclonal selection in TRACERx

Lung cancer is the leading cause of cancer-associated mortality worldwide. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource

The evolution of non-small cell lung cancer metastases in TRACERx

Metastatic disease is responsible for the majority of cancer-related deaths. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse