Whether a device uses ARM or FPGA technology depends on the specific application. This article will discuss the various characteristics of hybrid ARM FPGA devices and their differences. The characteristics of these devices include their performance, parallel processing, Hardware interfaces, Embedded software, and Off-chip memory. We will also discuss the pros and cons of both these technologies. Read on to learn more.
Off-chip memory limits performance
FPGAs are a new type of integrated circuit that combine logic blocks with embedded microprocessors and peripherals. This architecture closely resembles the SB24 chip developed by Burroughs Advanced Systems Group in 1982. Some FPGAs also incorporate embedded microprocessors, such as the 800 MHz dual-core ARM Cortex-A9 MPCore. ARM FPGA devices have an off-chip memory.
Off-chip memory is a major limitation of FPGA performance. The off-chip memory is too small to store all the necessary data. However, we can solve this problem by using a pipeline of kernels. This improves the overall performance of hybrid ARM FPGA devices. However, this technology has many limitations. In addition, off-chip memory limits performance of hybrid ARM FPGA devices.
The best batch size depends on balancing computation and communication time. For case one and case two, the optimal batch size is 64. In case three, the performance equations are complex. It is safe to assume that the optimal batch size is 64. However, if the performance is dependent on memory size, increasing the batch size can lead to a reduction in overall performance. This is because of the consumption of on-chip memory.
The CPU and FPGA can help accelerate Machine Learning algorithms. OpenCL, High Level Synthesis, and off-chip communications are key to this strategy. Those algorithms which are dependent on data object or loop unrolling can benefit from multiple compute units and parallel processing.
As machine-generated data continues to grow at an accelerating rate, the need for multicore computing becomes increasingly pressing. In response, design teams are rethinking the way data is moved. The fastest way to move data is through the FPGA. Further, the FPGA-based implementation of an ARM FPGA architecture can increase performance while keeping power consumption low.
Parallel processing
The idea of combining CPU and GPU performance in a single chip is nothing new. Altera has been combining ARM processors with FPGAs for years, and Intel could have done the same with its Xeon D X86 chips. Intel showed a second-generation hybrid CPU-FPGA testbed in March 2016.
However, many overlay designs are not suitable for FPGA architecture, and suffer from significant performance and area overhead. To overcome this problem, an efficient overlay architecture is necessary that is built around the capabilities of the FPGA and combines them with a high-level design approach. For this purpose, OpenCL will to address some of these problems. For example, OpenCL is a high-level programming language that addresses many design productivity problems.
To define FPGA circuits, designers use two types of design languages: schematic and hardware description language (HDL). The former is more suitable for larger structures and allows numerical specification of complex circuits. In addition, schematic entry is easier to visualize. However, it is not the only way to implement parallelism. Therefore, this guide is not a substitute for an expert’s opinion. For those who don’t have the time to learn HDL, there are other alternatives available on Rayming PCB & Assembly.
A recent benchmark, Vivado 2014.2, targets the Zynq FPGA fabric. The results show three scenarios with different workloads. Each scenario shows how long a PAR process takes in both cases. The overlay-based approach gives more time to process a single-core task, while the dual-core, block-RAM-based approach provides higher performance for higher-end workloads.
While the two-core ARM FPGA device is a great choice for many computing applications, it is still necessary to consider the software that will operate on these processors. The same is true for specialized accelerator circuits in hybrid ARM FPGA devices.
Hardware interfaces
ARM FPGA devices support different hardware interfaces. For example, CODEZERO uses the Zynq-7000 extensible processing platform, which includes a change to the memory layout, driver modifications, and hardware-dependent peripheral initialization. This approach is the first step toward enabling CODEZERO. The following sections explain how CODEZERO works.
This thesis proposes an approach to embedded virtualization using the microkernel-based CODEZERO hypervisor. The modified hypervisor can support hybrid SW-HW virtualization. The research includes a platform framework and case studies to test the effectiveness of the modified hypervisor. The proposed framework will allow the CPU to run hardware tasks under the control of the FPGA. The work will help designers develop and implement hybrid ARM FPGA applications.
The reconfigurable fabric supports the use of hypervisor control of regions. Contexts move from main memory to the context frame buffer under DMA control. The reconfigurable fabric also supports the use of a reconfigurable fabric. The Context Frame Buffer provides large storage capacity, and DMA control is available for data-flow. The IF can lock for hardware contexts or unlocked for another.
A mixed signal FPGA contains peripheral DACs and ADCs. These devices also operate as a system-on-a-chip. Their hybrid nature allows them to blur the line between FPGA and FPAA. The FPGAs have dual-port interfaces and dual-core processors. However, their architecture is not completely free from software complexities. So, there are numerous applications of hybrid ARM FPGA devices.
Embedded software
Embedded systems are systems that are designed to perform a specific task, rather than be general-purpose computers. They are often constrained by real-time performance requirements due to safety or usability concerns. Often, a hybrid device combines a single chip with several programmable logic controllers. These devices are typically in equipment racks or across large distances. If they must operate continuously, they must be able to run on a backup system.
The underlying technology enables the use of a microkernel-based hypervisor on a hybrid ARM FPGA device. This thesis also includes modifications to CODEZERO, a commercially available hypervisor that supports hybrid SW-HW virtualization. A platform framework was developed to test the modified hypervisor and case studies were designed to validate its performance.
Embedded systems can be simple or complex, ranging from no user interface to complex graphical displays. For example, a simple embedded device might have buttons, LEDs, or a menu system. More advanced devices may use a graphical screen, touch sensing, and screen-edge soft keys. In this case, buttons and other elements change their meaning in relation to the screen, making selections simpler.
The MMU-FPGA device’s peripherals are enabled in separate processes. In this case, the primary core will initiate the MMU first and map peripheral registers to virtual addresses later. The secondary core will then send a signal to the primary core. This is a key feature of hybrid architectures. In addition, hybrid platforms are challenging for automated accelerator generation. With COMRADE techniques, the creation of a single code for a hybrid device can be automated.
As a result, an application must be carefully designed to minimize the risk of security breaches. This means implementing appropriate security measures before using the hybrid FPGA-ARM device. Unfortunately, today’s security measures may not be considered secure in a decade.
