Femtosense
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About
Femtosense provides an embedded AI platform built on a unique AI accelerator. It offers higher performance at lower latency and reduced energy consumption, all within a smaller silicon footprint. Their technology is based on neuromorphic concepts of sparsity and locality, resulting in 100x energy efficiency and 10x model capacity improvements. The platform includes pre-trained and pre-optimized AI models, an SDK for custom application creation, and the Sparse Processing Unit (SPU) AI accelerator. Applications range from wearables and home appliances to robotics and autonomous vehicles.
Platform
Task
Features
• ai accelerator
• software development kit (sdk)
• embedded ai platform
• sparse ai models
• ready-to-deploy models
• sparsity-aware silicon
Job Opportunities
Deep Learning Compiler Engineer
Femtosense's embedded AI platform delivers high-performance, low-latency AI with 100x energy efficiency and 10x model capacity.
Education Requirements:
BS
MS
PhD in Computer Science, Electrical Engineering, or a related field
Experience Requirements:
2+ years of experience or equivalent experience in a related PhD or MS program
Other Requirements:
Strong experience in C++ and Python development, with a focus on compiler or systems programming
Solid understanding of compiler design, optimization passes, and low-level hardware constraints
Familiarity with Mixed-Integer Linear Programming
Hands-on experience with MLIR or other intermediate representations (e.g., TVM or LLVM)
Responsibilities:
Develop and optimize compiler passes, including graph partitioning, memory allocation, and other critical performance enhancements
Work at the intersection of software and hardware to ensure seamless translation of deep learning models into efficient, low-level code in a custom ISA
Collaborate closely with the hardware and algorithm teams to support unique features and capabilities of our AI accelerators
Design and implement custom MLIR dialects tailored for our Sparse Processing Unit (SPU)
Explore advanced optimization techniques for complex scheduling and resource allocation problems
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