Four soft-core processors for embedded systems
Sven-Ake Andersson, Realtime Embedded
EETimes (1/8/2013 3:23 PM EST)
Since 2000, the folks at Realtime Embedded have concentrated on helping companies develop embedded systems used in advanced products. Their four primary focus areas are FPGA, Linux, virtual hardware, and multicore processing systems. Realtime Embedded is involved in customer and financed research projects, in house and on site, spanning a wide range of industries.
Many of you may have already read my blog called How to design an FPGA from scratch, which I started to write 2006 and which Max Maxfield wrote about in EE Times for the first time in 2007.
My latest blog describes the work I have performed at Realtime Embedded over the course of the past year. In this blog, I investigate four soft-core processors and use the same setup as in my first blog called “learning by doing.” This means that each soft processor will be implemented in an FPGA and the whole design process will be documented.
Many of you may have already read my blog called How to design an FPGA from scratch, which I started to write 2006 and which Max Maxfield wrote about in EE Times for the first time in 2007.
My latest blog describes the work I have performed at Realtime Embedded over the course of the past year. In this blog, I investigate four soft-core processors and use the same setup as in my first blog called “learning by doing.” This means that each soft processor will be implemented in an FPGA and the whole design process will be documented.
To read the full article, click here
Related Semiconductor IP
- Chiplet Die-to-Die Interconnect IP Solution
- High speed MACsec Engine 100G/200G/400G/800G/1.6T
- Temperature/Voltage sensors
- AMBA Bus Host to eSPI Controller/Target
- AMBA Bus Host to eSPI Controller
Related Articles
- ACE: Confidential Computing for Embedded RISC-V Systems
- Efficient Hardware-Assisted Heap Memory Safety for Embedded RISC-V Systems
- Android, Linux and Real-Time Development for Embedded Systems
- NAND Flash memory in embedded systems
Latest Articles
- ZK-Flex: A Flexible and Scalable Framework for Accelerating Zero-Knowledge Proofs
- ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training
- OpenEye: A Scalable Open-Source Hardware Accelerator for DNNs
- CHIMERA: A Flexible and Scalable 3.1 TOPS/W AI-MCU with Transformer Accelerator and 563 Gb/s Shared-L2 Memory Subsystem with QoS Guarantees
- CXL-ClusterSim: Modeling CXL-based Disaggregated Memory Cluster for Pooling and Sharing using gem5 and SST