Control an FPGA bus without using the processor
Noe Quintero, Linear Technology
EDN (April 27, 2016)
Many FPGA designs use an embedded processor for control. A typical solution involves the use of a soft processor such as a Nios, though FPGA SoCs with a built-in hard processor have become popular too. Figure 1 shows a typical Altera FPGA system that contains the processor and a mix of peripherals that are connected via Altera’s Avalon Memory Mapped (MM) bus. These processors greatly simplify the end application, but require a strong programing background and knowledge of complicated toolchains. This can hinder debug, especially if a hardware engineer needs a simple way to read and write to the peripherals without pestering the software engineer.
This Design Idea uses Altera's SPI Slave to Avalon MM Bridge to provide a simple way to hop onto the Avalon bus. There are two advantages to this technique: It does not compromise the original system design, and the bridge can co-exist with the embedded processor. For the system shown in Figure 1, the SPI bridge allows the engineer to directly control the frequency of the LTC6948 fractional-N PLL, set the LTC1668 DAC voltage, read a voltage from the LTC2498 ADC, or read temperatures from the LTC2983, just like the processor can.
To read the full article, click here
Related Semiconductor IP
- DSP-Based 112G SerDes
- XTAL oscillator in TSMC-7nm
- GPU
- V-by-One Verification IP
- AI model compression IP
Related Articles
- Add graphics without using a dedicated graphics controller
- Simplifying SoC design with the Customizable Control Processor
- Implementing Field-oriented Brushless Motor Control Using an ARM7 Processor
- Embedded FPGA design without hard barriers using OpenBus
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