High throughput spatial convolution filters on FPGAs

Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30–60 FPS, while maintaining functional flexibility

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Multi-core CPU based solutions have demonstrated their inadequacy for this problem due to the memory wall and low parallelism. Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory. FPGA design solutions are also actively being explored, which allow implementing the memory hierarchy using embedded BlockRAM. This boosts the parallel use of shared memory elements between multiple processing units, avoiding data replicability and inconsistencies. This makes FPGAs potentially powerful solutions for real-time classification of CNNs. Both Altera and Xilinx have adopted OpenCL co-design framework from GPU for FPGA designs as a pseudo-automatic development solution. In this paper, a comprehensive evaluation and comparison of Altera and Xilinx OpenCL frameworks for a 5-layer deep CNN is presented. Hardware resources, temporal performance and the OpenCL architecture for CNNs are discussed. Xilinx demonstrates faster synthesis, better FPGA resource utilization and more compact boards. Altera provides multi-platforms tools, mature design community and better execution times

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Multipumping flexible DSP blocks for resource reduction on Xilinx FPGAs

For complex datapaths, resource sharing can help reduce area consumption. Traditionally, resource sharing is applied when the same resource can be scheduled for different uses in different cycles, often resulting in a longer schedule. Multipumping is a method whereby a resource is clocked at a frequency that is a multiple of the surrounding circuit, thereby offering multiple executions per global clock cycle. This allows a single resource to be shared among multiple uses in the same cycle. This concept maps well to modern field-programmable gate arrays (FPGAs), where hard macro blocks are typically capable of running at higher frequencies than most designs implemented in the logic fabric. While this technique has been demonstrated for static resources, modern digital signal processing (DSP) blocks are flexible, supporting varied operations at runtime. In this paper, we demonstrate multipumping for resource sharing of the flexible DSP48E1 macros in Xilinx FPGAs. We exploit their dynamic programmability to enable resource sharing for the full set of supported DSP block operations, and compare this to multipumping only multipliers and DSP blocks with fixed configurations. The proposed approach saves on average 48% DSP blocks at a cost of 74% more LUTs, effectively saving 30% equivalent LUT area and is feasible for the majority of designs, in which clock frequency is typically below half the maximum supported by the DSP blocks

Minimizing DSP block usage through multi-pumping

Resource sharing in the mapping of an algorithm to an architecture allows the same resource to be scheduled for different uses in different cycles, generally at the cost of increased schedule length. Multi-pumping is a method whereby a resource is clocked at a frequency that is a multiple of the surrounding circuit, thereby offering multiple executions per global clock, and therefore sharing in the same clock cycle. This concept maps well to FPGA architectures, where hard macro blocks are typically capable of running at higher frequencies than standard logic. While this technique has been demonstrated for multipliers, modern DSP blocks are more complex with multiple computational nodes. In this paper, we apply multi-pumping to minimise DSP block usage, while taking advantage of the multiple nodes they support. The proposed approach uses, on average, 39% fewer DSP blocks, at a cost of 19% more LUTs and 7% more registers

Building Blocks for Spikes Signals Processing

Neuromorphic engineers study models and implementations of systems that mimic neurons behavior in the brain. Neuro-inspired systems commonly use spikes to represent information. This representation has several advantages: its robustness to noise thanks to repetition, its continuous and analog information representation using digital pulses, its capacity of pre-processing during transmission time, ... , Furthermore, spikes is an efficient way, found by nature, to codify, transmit and process information. In this paper we propose, design, and analyze neuro-inspired building blocks that can perform spike-based analog filters used in signal processing. We present a VHDL implementation for FPGA. Presented building blocks take advantages of the spike rate coded representation to perform a massively parallel processing without complex hardware units, like floating point arithmetic units, or a large memory. Those low requirements of hardware allow the integration of a high number of blocks inside a FPGA, allowing to process fully in parallel several spikes coded signals.Junta de Andalucía P06-TIC-O1417Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Ciencia e Innovación TEC2006-11730-C03-0