Alif Is Creating SoC Solutions for Machine Learning with Cadence and Arm
October 2, 2023
Alif Semiconductor is bridging the gap between standard microcontrollers and high-end GPU solutions. They’re providing scalable, integrated, and secure microprocessors and microcontrollers for low-power machine learning (ML) tasks. To design these complex SoCs, Alif is using Cadence’s state-of-the-art EDA tools as well as the Arm® Cortex® and Ethos processors.
While standard MCUs can address simple ML tasks like keyword spotting or failure detection, tasks like facial or speech recognition are much heavier and require hundreds of giga operations per second (GOPS). Standard MCUs can’t deliver that. Designers would have to jump to the GPU classes to achieve that kind of performance, but they’re too costly, too big, and consume too much power.
Related Semiconductor IP
- AES GCM IP Core
- High Speed Ethernet Quad 10G to 100G PCS
- High Speed Ethernet Gen-2 Quad 100G PCS IP
- High Speed Ethernet 4/2/1-Lane 100G PCS
- High Speed Ethernet 2/4/8-Lane 200G/400G PCS
Related Blogs
- SoC QoS gets help from machine learning
- Take your neural networks to the next level with Arm's Machine Learning Inference Advisor
- Accelerating Machine Learning Deployment with CEVA Deep Neural Network (CDNN)
- NetSpeed Leverages Machine Learning for Automotive IC End-to-End QoS Solutions
Latest Blogs
- Why Choose Hard IP for Embedded FPGA in Aerospace and Defense Applications
- Migrating the CPU IP Development from MIPS to RISC-V Instruction Set Architecture
- Quintauris: Accelerating RISC-V Innovation for next-gen Hardware
- Say Goodbye to Limits and Hello to Freedom of Scalability in the MIPS P8700
- Why is Hard IP a Better Solution for Embedded FPGA (eFPGA) Technology?