Semiconductor options for real-time signal processing
By Leon Adams, Texas Instruments -- EDN, 11/25/2004
Designers of real-time-signal-processing systems face lots of options and an ever-changing technology landscape.
If a universal semiconductor component existed that could allow engineers to realize every real-time-signal-processing system with optimum price, performance, power and function, then selection would be automatic. However, the reality is that designers of such systems must carefully evaluate many device options within a continually changing technology landscape. Although engineers typically associate real-time signal processing with programmable DSPs, the market today has many options. Designers face myriad core technologies, all of which claim to be the best for executing real-time operations for a given application. An engineer's task is choosing what delivers the best mix of performance, size, power consumption, features, and development tools—all without breaking the budget.
Of the chief candidates for real-time signal processing, this article examines the benefits of these technologies, including ASICs, ASSPs, (application-specific signal processors), FPGAs, and RISC processors, and compares the design trade-offs among them.
Click here to read more ....
Related Semiconductor IP
- DSP-Based 112G SerDes
- XTAL oscillator in TSMC-7nm
- GPU
- V-by-One Verification IP
- AI model compression IP
Related Articles
- IP-based Toolbox for Digital Signal Processing Reuse: Application to Real-time Spike Sorting
- Reconfiguring Design -> Reconfigurable computing aims at signal processing
- Reconfigurable signal processing key in base station design
- No size fits all for signal processing on FPGA (RF Engines)
Latest Articles
- SEAM-V: A Hybrid-Decoupled RISC-V Vector Processor with Backend-Visible EP Context for Sustained Vector Throughput
- New Number Formats for FFT IP Cores in Optical OFDM Transceivers
- Reducing Power Consumption of Embedded Dynamic Memories with ECCs
- NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
- A 32-channel event-based bio-signal analog front-end with adaptive delta and pulse frequency encoding