Driving up efficacy in medical devices
The driving force behind microprocessor evolution has, for decades, primarily been the needs of the IT sector and personal computing, the roadmap provided by leading manufacturers has guaranteed allegiance to a common architecture; support that has been mutually beneficial to all.
As the needs of traditional applications broadened, processor manufacturers responded, by extending and enhancing the core architecture while retaining code compatibility, allowing for easy migration as more powerful solutions became available. While the capabilities and functionalities evolved, however, one parameter above all others had to be preserved; increased performance. As a result of constant improvement in their performance, over the last decade or so these same processors have slowly but inexorably migrated to other applications, delivering a level of performance that now enables many established and emerging applications.
More recently the need to deliver that performance within tighter power budgets has become apparent, the cost of greater performance has traditionally been increased power, but as integrated circuits become more power-hungry their ability to dissipate the subsequent heat diminished. The performance they could provide was becoming physically limited by the power needed to deliver it, a trend that was unsustainable. In a bid to overcome this impending roadblock processor manufacturers had to radically re-engineer their fundamental manufacturing processes, a difficult challenge but one that has ultimately resulted in even greater architectural innovations; each one widening the sphere of potential applications.
The medical sector is one of the many applications that has benefited from processor innovations and greater focus on the particular requirements of the embedded market. Subsequently many of the features we have become accustomed to in one market are now being made available in others, providing the same productivity gains and improved user experiences.
Medical devices, for example, are predominantly designed to be uni-functional; perform one task very well. Their ability to perform that task — the efficacy of the device — can be measured by the quality of the results and in health care there is no such thing as ‘good enough’; when it comes to diagnosis or health care administration there’s always room for improvement.
One of the ways greater performance can deliver overall improvement is in the addition and consolidation of functions. The ability of modern processors to perform multiple functions at the same time is a direct result of the emergence of multicore processing.
In medical devices, this translates to greater capability in uni-function devices, thereby raising the overall efficacy of the equipment. What may have once required several processors attempting to work together can now be accomplished on a single platform running more sophisticated software with greater efficiency.
HSA & OpenCL
The trend to integrate more cores and capability into general purpose microprocessors has significant benefits in the medical sector. Graphics processing units are fundamentally very powerful co-processors that run in parallel to each other and the main processor. As a result they can, in principle, be applied to any massively parallel data processing tasks.
Using graphical processing units for ‘general purpose’ tasks is an emerging trend and has become known simply as GPGPU. Medical imaging equipment generates vast amounts of data, all of which needs to be processed, and so GPGPU is particularly applicable in this application.
GPGPU is enabled by a number of open source software projects such as OpenGL and OpenCL, that allow algorithms to be ported to practically any processing architecture; important because graphics processors are typically designed for maximum performance at the cost of architectural continuity. The OpenCL project uses a level of abstraction that allows algorithms to be ported to any architecture that supports the language and it has rapidly become the ‘default’ approach for GPGPU.
Using GPGPU in medical applications makes sense, because it allows data that is easily parallelised to be processed in a more efficient flow. Established processor architectures, even multicore processors, are typically executing software written in C, which doesn’t inherently support parallelism; while many tasks can be executed simultaneously, even the same task, GPGPU is better suited to raw ‘data crunching’. This increased throughput can have a direct impact on the efficacy of medical imaging equipment or any device that relies on processing raw data as it’s acquired, in real time.
Many processors, such as AMD’s latest x86-based Embedded G-Series processors, go so far as to combine the CPU and GPU elements in an architecturally optimised format, which AMD calls Accelerated Processing Units (APUs).
Devices that integrate multiple but different processing cores on a single device are being termed as Heterogeneous System Architectures and their development is now supported by the HSA Foundation, an open industry standards body founded by AMD, ARM, Imagination Technologies, MediaTek, Texas Instruments, Samsung Electronics and Qualcomm.
Through HSA, applications can create data structures in a single unified address space, using the hardware features most appropriate for the task. The common platform also means sharing data between elements is simplified, allowing multiple tasks to work on the same coherent memory region while maintaining data synchronisation.
The HSA Foundation’s goal is to ease the integration of heterogeneous elements while facilitating data sharing. It achieves this, in part, by employing a common standard low-level interface layer, called the HSA Intermediate Language, providing a single target for low-level software and tools. The layer is flexible enough to allow for vendor differentiation at a hardware level, while removing the programming burden of targeting different architectures. AMD now offers HSA-optimised tools for leading heterogenous languages, OpenCL and C++ AMP.
OpenCL is a technique specifically intended to accelerate parallel tasks, as opposed to the way tasks are executed sequentially in other high level languages. It achieves this through its structure of creating kernels (which are akin to functions in C), grouping multiple kernels into programs and creating applications that control the execution of those programs. Specific tasks are broken down into work-items, which are able to execute in parallel. Groups or work-items are termed ‘workgroups’, which are typically executed on a single multicore platform where they are able to share local resources, such as memory. As such, OpenCL is less about the semantics of the language and more about how algorithms are constructed to target platform with parallel resources (such as multiple CPU/GPU cores on the same device).
These power- or performance-optimised processors are able to deliver unprecedented levels of processing performance while dissipating extremely low power levels, which means they are able to operate in a fanless environment and even in hermetically sealed equipment, ideal for the demanding conditions often associated with medical equipment that can require regular and extensive sterilisation.
The User Experience
Another major trend across all markets in the embedded space is the evolution of the user interface. User expectation is that if a screen is present it will be high resolution and interactive. Bringing those features to medical devices makes equipment more intuitive and simpler to operate, thereby improving not only the user experience for the medical professional but also overall patient care. Far from being a simple ‘gimmick’ user interfaces are an important part of product development, with entire teams devoted to creating the right solution.
The reason for this is the massive productivity gains a good UI can deliver; something as simple as support for multiple languages or different font sizes can influence the overall efficacy of any device, a factor that is perhaps even more crucial in a medical environment.
User interfaces today invariably comprise a high-resolution display and a multi-touch interface, features that would have until recently required dedicated devices. However, many of the latest microprocessors are now able to directly support sophisticated user interfaces though either dedicated hardware blocks closely coupled to the central processor, or dedicated interfaces connected to high-speed processor buses and external drivers.
Microprocessors have always been well equipped to drive displays and as the resolution of display technology has increased so too has the ability of microprocessors to make best use of that resolution. This is a feature that can be seen to directly improve medical equipment in terms of imaging applications such as ultrasound equipment and even MRI and CT scanners.
The ability to provide faster image manipulation at higher resolutions has become fundamental to all aspects of medical care. The general trend within microprocessor design is to add greater graphics capabilities with every new generation, an aspect that is parallel to the development of multicore architectures.
The COM Approach
Using high-performance processors in embedded applications delivers massive benefits in terms of capabilities and performance. However, the real ‘value’ in a medical device isn’t in its integration of ‘general purpose’ processors and sub-systems; the real value is in the results they deliver.
Integrating a high-performance x86 processor is also challenging for engineers unfamiliar with the devices; it requires a level of familiarity with the architecture that can take many man-hours to acquire. Predominantly, engineers would need a fairly standard processor and sub-system on which to base their device and it is here where the trend for Computer-on-Module can deliver.
Computer-on-Module, or COM, is a format that is widely used in many vertical sectors, including medical. It provides a standard platform which is inherently upgradeable as higher performance processors become available, but equally flexible in moving to more power-efficient platforms if an OEM needs to retarget a device for, by way of example, the portable medical device market.
The AMD Embedded G-Series System-on-Chip is available in dual- and quad-core variants and congatec has used this powerful platform to develop its Type 6 COM Express Compact conga-TCG family. The SOC integrates the AMD Radeon graphics processors to deliver several GFlops of processing power, while the dual-core version dissipates as little as 9W and the quad-core just 25W.
For applications that demand more processing power, congatec uses the AMD Embedded R-Series APUs in its COM Express Basic conga-TFS solution.
Through a COM-approach to medical device development, OEMs have access to a proven, high-performance processing platform that has an inherent migration path, backed by the assurance of five plus two years continuity of supply. A modular approach to design also simplifies the certification process for product variants, providing even greater commercial advantage for manufacturers.
Zeljko Loncaric is a Marketing Engineer with congatec AG.
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