Brain Derived Vision Algorithm on High Performance Architectures

Even though computing systems have increased the number of transistors, the switching speed, and the number of processors, most programs exhibit limited speedup due to the serial dependencies of existing algorithms. Analysis of intrinsically parallel systems such as brain circuitry have led to the identification of novel architecture designs, and also new algorithms than can exploit the features of modern multiprocessor systems. In this article we describe the details of a brain derived vision (BDV) algorithm that is derived from the anatomical structure, and physiological operating principles of thalamo-cortical brain circuits. We show that many characteristics of the BDV algorithm lend themselves to implementation on IBM CELL architecture, and yield impressive speedups that equal or exceed the performance of specialized solutions such as FPGAs. Mapping this algorithm to the IBM CELL is non-trivial, and we suggest various approaches to deal with parallelism, task granularity, communication, and memory locality. We also show that a cluster of three PS3s (or more) containing IBM CELL processors provides a promising platform for brain derived algorithms, exhibiting speedup of more than 140 × over a desktop PC implementation, and thus enabling real-time object recognition for robotic systems

R.: Novel brain-derived algorithms scale linearly with number of processing elements

Algorithms are often sought whose speed increases as processing elements are added, yet attempts at such parallelization typically result in little speedup, due to serial dependencies intrinsic to many algorithms. A novel class of algorithms have been developed that exhibit intrinsic parallelism, so that when processing elements are added to increase their speed, little or no diminishing returns are produced, enabling linear scaling under appropriate conditions, such as when flexible or custom hardware is added. The algorithms are derived from the brain circuitry of visual processing 10, 17, 8, 9, 7. Given the brain’s ability to outperform computers on a range of visual and auditory tasks, these algorithms have been studied in attempts to imitate the successes of real brain circuits. These algorithms are slow on serial architectures, but as might be expected of algorithms derived from highly parallel brain architectures, their lack of internal serial dependencies makes them highly suitable for efficient implementation across multiple processing elements. Here, we describe a specific instance of an algorithm derived from brain circuitry, and its implementation in FPGAs. We show that the use of FPGAs instead of general-purpose processing elements enables significant improvements in speed and power. A single high end Xilinx Virtex 4 FPGA using parallel resources attains more than

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders