How to Reduce Code Size (and Memory Cost) Without Sacrificing Performance
Embedded.com
Nov 29 2005 (17:55 PM)
Today's intelligent compilers offer many options for squeezing more performance out of application code. Many of these optimizations, however, tend to increase overall code size.
As a result, once developers of optimized application code have reached the required performance specifications, there still remains the challenge of bringing code size back under control.
Through an iterative process of building application code using different compiler optimization options and profiling the result, developers can hone in and identify infrequently used and non-critical sections of code to trade off performance where it matters least for reduced code size, providing minimal impact on system performance. Often, varying compiler options to reduce code size can enable developers to decrease the amount of on-chip and external memory an application requires without adversely affecting performance, thereby reducing the overall bill of materials (BOM).
To read the full article, click here
Related Semiconductor IP
- Wi-Fi 7(be) RF Transceiver IP in TSMC 22nm
- PUF FPGA-Xilinx Premium with key wrap
- ASIL-B Ready PUF Hardware Premium with key wrap and certification support
- ASIL-B Ready PUF Hardware Base
- PUF Software Premium with key wrap and certification support
Related White Papers
- How silicon and circuit optimizations help FPGAs offer lower size, power and cost in video bridging applications
- Minimize IC power without sacrificing performance
- How to build reliable FPGA memory interface controllers without writing your own RTL code!
- How to Reduce FPGA Logic Cell Usage by >x5 for Floating-Point FFTs
Latest White Papers
- e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications
- How to design secure SoCs, Part II: Key Management
- Seven Key Advantages of Implementing eFPGA with Soft IP vs. Hard IP
- Hardware vs. Software Implementation of Warp-Level Features in Vortex RISC-V GPU
- Data Movement Is the Energy Bottleneck of Today’s SoCs