Algorithm developers are usually using double precision data types to be able to focus on the mathematical functionality of the algorithm. When this algorithm is implemented as a hardware module, the data accuracy must be reduced to minimum number of bits that still fulfills the system performance requirements. The process of converting the floating-point algorithm to bit-level optimized model is complicated and requires special knowledge. This webinar introduces a simple and robust quantization methodology based on value range analysis.

What You Will Learn

  • What is fixed-point conversion a.k.a quantization
  • Dynamic and static quantization methods
  • Handling special cases
  • Using Catapult Value Range Analysis feature for quantizing HLS design

Who Should Attend

  • Algorithm developers
  • HLS designers
  • HW designers
  • Verification Engineers

Meet the Speaker

Siemens EDA

Petri Solanti

Senior Application Engineer

Petri Solanti is a senior application engineer at Siemens, with an HLS and low-power tools focus. He is a designer and application engineer with over 25 years of experience in Electronics System-Level design tools and methodologies. His areas of interest include design methodologies from algorithm to RTL, system analysis and HW/SW co-design. Prior to Mentor, Mr. Solanti held application engineer positions at Cadence, CoWare, Synopsys and MathWorks. He received his MScEE degree from Tampere University of Technology, Finland.

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