With the new cores, customers will be able to customise processor designs using Codasip Studio tools to support challenging tasks such as neural networks (AI/ML) even in smaller, power-constrained applications – such as IoT edge.
Running AI/ML in edge IoT/IIoT devices improves security and power consumption and helps to reduce latency for real-time processing. However, algorithms for AI/ML are computationally intensive and custom processors are needed to deliver sufficient performance with the limited resources available in such embedded systems.
To enable this, Codasip’s embedded cores L31/L11 run Google’s TensorFlowLite for Microcontrollers combined with Codasip Studio tools to customise a new type of Embedded AI cores which are suited for IoT applications where both space and power are at a premium.
Codasip CTO, Zdeněk Přikryl commented, “Licensing the CodAL description of a RISC-V core gives Codasip customers a full architecture license enabling both the ISA and microarchitecture to be customised. The new L11/31 cores make it possible to add features our customers were asking for, such as edge AI, into the smallest, lowest power embedded processor designs.”
In addition to making the cores easier to customise to match specific embedded designs, Codasip has also enhanced both of the new cores to support significantly higher frequencies.
AI and ML applications are not well suited to off-the-shelf processors. The data types, the quantization and performance needs of the devices differ significantly from application to application. Codasip’s Design for Differentiation approach means customers using its Studio tools are able to customise the processor for its specific system, software and application requirements.
Similarly, embedded devices in low power IoT applications are extremely resource-constrained: limited in memory and with a limited instruction set. However, developers need them to be low power, inherently secure, and able to respond and communicate in real-time.
Codasip’s latest L31 and L11 processor cores are the first to feature TFLite Micro support, but the support is also being made available across Codasip’s entire portfolio of RISC-V cores.
With the support for Neural Networks using the TensorFlow Lite AI framework, Codasip RISC-V processor IP will, according to the company, help system developers to embed improved levels of performance at the core of their AI/ML device.
