Responsibilities:
· Understand the mainstream GPGPU architecture and their design choice
· Using existing ML or HPC background to propose the possible architecture improvements and exploration possibilities
· Can deeply understand the programming model of each architecture and its impacts and influence on eco-system
· Maintain and build the confidence model for both verifying with software tools and profiling the performance so at to understand different architecture design choices
Requirement:
· Bachelor’s with relevant experience in Domain Specific Accelerators/Machine Learning
· Knowledge of different machine learning algorithms (eg: CNN, LSTM, DNN, GNN etc) and their execution model in GPU/CPU; understanding of parallel execution models like SIMD, SIMT etc
· Familiarity with GPGPU (eg: CUDA/OpenCL) programming models is preferred but not mandatory
· Any prior experience in developing simulation-based performance models for domain specific accelerators
· Knowledge in design principles, colour theory and typography
· Knowledge and experience in running user research, ideation workshops and usability tests
· Proficient in C/C++ and scripting languages (Perl/Python)
· Knowledge in one or more AI programming frameworks – Tensorflow, Pytorch etc
· Expertise with popular design tools and software such as Figma, Miro, Adobe Creative Suite.
· Knowledge of RISC-V ISA is valuable but not mandatory
· Ability to deliver high quality analysis/results and independently drive modeling tasks is preferred
· Good knowledge of USB2.0/USB3.0/ USBCV protocols and USB Device Classes (HID, MSC, CDC, UVC, UAC, DFU, Custom).
· Curiosity and Enthusiasm to explore advance state of the art technologies with calculated risks