An Independent Analysis Altera’s FPGA Floating-point DSP Design Flow

Altera开发了一种新的浮点设计流程,旨在简化Altera FPGA上浮点数字信号处理算法的实现过程,并使其达到更高的性能和效率。该流程通过生成融合的数据路径来消除传统FPGA设计中的冗余,采用高级模型化的设计流程,利用Altera的DSPBuilder Advanced Blockset和MATLAB及Simulink工具。BDTI进行了独立评估,验证了此设计流程的有效性和易用性。

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原文链接:http://www.altera.com.cn/literature/wp/wp-01166-bdti-altera-floating-point-dsp.pdf

后续按照文章测试方法,做一个Xilinx DSP架构FPGA对比测试,就更有意义了。

不过记住:人脑是最好的优化器!


简介如下:

OVERVIEW
FPGAs are increasingly used as parallel processing engines for demanding digital 
signal processing applications. Benchmark results show that on highly parallelizable 
workloads, FPGAs can achieve higher performance and superior cost/performance 
compared to digital signal processors (DSPs) and general-purpose CPUs. However, to 
date, FPGAs have been used almost exclusively for fixed-point DSP designs.  FPGAs 
have not been viewed as an effective platform for applications requiring high-performance 
floating-point computations.  FPGA floating-point efficiency and performance has been 
limited due to long processing latencies and routing congestion. In addition, the traditional 
FPGA design flow, based on writing register-transfer-level hardware descriptions in 
Verilog or VHDL, is not well suited to implementing complex floating-point algorithms. 
Altera has developed a new floating-point design flow intended to streamline the 
process of implementing floating-point digital signal processing algorithms on Altera 
FPGAs, and to enable those designs to achieve higher performance and efficiency than 
previously possible. Rather than building a datapath consisting of elementary floatingpoint operators (for example, multiplication followed by addition followed by squaring), the 
floating-point compiler generates a fused datapath that combines elementary operators 
into a single function or datapath.  In doing so, it eliminates the redundancies present in 
traditional floating-point FPGA designs. In addition, the Altera design flow is a high-level 
model-based flow using Altera’s DSP Builder Advanced Blockset and the MathWorks’ 
MATLAB and Simulink tools.  Altera hopes that by working at a high level, FPGA 
designers will be able to implement and verify complex floating-point algorithms more 
quickly than would be possible with traditional HDL-based design. 
BDTI performed an independent analysis of Altera’s floating-point DSP design 
flow.  BDTI’s objective was to assess the performance that can be obtained on Altera 
FPGAs for demanding floating-point DSP applications, and to evaluate the ease-of-use of 
Altera’s floating-point DSP design flow. This paper presents BDTI’s findings, along with 
background and methodology details.

### Floating-point IP in Computing or Networking Context In the context of computing, floating-point IP typically refers to intellectual property related to algorithms, designs, or implementations that handle floating-point arithmetic operations. These are critical components for processors, GPUs, and other computational devices where precision is essential. Floating-point units (FPUs), which perform calculations involving non-integer numbers, often rely on proprietary architectures designed by companies specializing in semiconductor design[^1]. For example, many modern CPUs incorporate advanced FPU designs licensed from third-party vendors who specialize in optimizing performance while minimizing power consumption. This type of IP may include patents covering specific techniques used during multiplication, division, square root extraction, transcendental functions evaluation among others[^2]. When discussing networking within this framework though it becomes less common but still possible depending upon application requirements such as scientific simulations over distributed systems requiring high accuracy mathematical computations across nodes connected via TCP/IP protocols stack implemented using socket programming under UNIX environments described earlier.[^3] For both scenarios mentioned above regarding either standalone embedded solutions needing efficient numerical processing capabilities without increasing silicon footprint significantly; Or large scale server farms executing complex workloads demanding consistent results regardless platform variations - having access rights through licensing agreements concerning relevant 'floating point' technologies could prove invaluable assets ensuring competitive advantage amongst peers operating similar marketspace conditions today! ```c // Example C code demonstrating basic usage of a library function performing float operation. #include <stdio.h> double add_floats(double num1,double num2){ return(num1+num2); } int main(){ double result; result=add_floats(0.1,0.2); // Simple addition showing how floats interacted correctly despite inherent limitations due representation errors etc.. printf("Result:%f\n",result); } ```
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