The embedded systems hardware ‘make or buy’ dilemma
Ready-made CPU modules are making increasing sense for handling technology complexity and unpredictable market conditions.
Today’s 16 and 32-bit microcontrollers have become so complicated that growing numbers of embedded developers are questioning whether it’s worthwhile building a system from scratch or whether they’d be better off buying-in the more tricky bits ready-made. The continuing unpredictable market conditions are adding further pressures to examine what makes sense to do in-house.
One option is to just buy all the hardware off-the-shelf and concentrate on the application. Another idea is to extend the life of a design by adopting a standard platform that you can re-use for various different projects. Particularly interesting is the rise of high-density CPU modules. These are CPUs plus sub-systems that come on a tiny board or, for higher volumes, a multi chip module (MCM) that can be treated like a big chip. The advantage is that someone else has done the difficult part of the design and so you can often get away with a relatively simple PCB for the rest of the system.
Read more ....
Related Semiconductor IP
- 8MHz / 40MHz Pierce Oscillator - X-FAB XT018-0.18µm
- UCIe RX Interface
- Very Low Latency BCH Codec
- 5G-NTN Modem IP for Satellite User Terminals
- 400G UDP/IP Hardware Protocol Stack
Related Articles
- Last-Time Buy Notifications For Your ASICs? How To Make the Most of It
- The Impact of Make vs Buy Decisions for Memory Interface Solutions
- To develop or buy a Verification IP
- Buy or Build an RTOS: Does it Matter for Medical Devices?
Latest Articles
- SNAP-V: A RISC-V SoC with Configurable Neuromorphic Acceleration for Small-Scale Spiking Neural Networks
- An FPGA Implementation of Displacement Vector Search for Intra Pattern Copy in JPEG XS
- A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA
- VMXDOTP: A RISC-V Vector ISA Extension for Efficient Microscaling (MX) Format Acceleration
- PDF: PUF-based DNN Fingerprinting for Knowledge Distillation Traceability