Key technologies and capabilities for this role
Common questions about this position
Candidates need 3+ years of proven experience in formal verification, along with hands-on experience with tools like Cadence JasperGold or Synopsys VC Formal.
Expertise in SystemVerilog Assertions (SVAs), solid experience with RTL design in Verilog or VHDL, proficiency in Python scripting, and understanding of compute architecture are required.
This information is not specified in the job description.
This information is not specified in the job description.
A Bachelor’s or Master’s degree in Electrical Engineering or Computer Engineering is required.
Enhances AI inference with at-memory computing
Untether AI enhances the speed and efficiency of AI inference workloads using at-memory computing. This method places the compute element next to memory cells, which boosts compute density and accelerates AI inference for various neural networks, such as those used in vision, natural language processing, and recommendation systems. The company targets businesses that rely on AI technologies and need high-performance computing for inference tasks. Their products, including the runAI200® devices and tsunAImi® accelerator cards, are designed to deliver exceptional performance, with the tsunAImi® card offering over 2 PetaOps. This allows businesses to optimize their AI workloads while maintaining a compact PCI-Express form factor. Untether AI's goal is to provide efficient and cost-effective solutions for companies looking to enhance their AI applications.