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Full-Time

Fabric SOC Architect

Posted on 10/17/2024

Tenstorrent

Tenstorrent

201-500 employees

Develops AI/ML hardware accelerators and software

Data & Analytics
Enterprise Software
AI & Machine Learning

Compensation Overview

$100k - $500kAnnually

Entry, Junior, Mid, Senior

Remote in USA

US Citizenship Required

Category
Hardware Engineering
System Hardware Engineering
Required Skills
C/C++
Data Analysis
Requirements
  • BS/MS/PhD in EE/ECE/CE/CS
  • Strong grasp of NoC topologies, routing algorithms, queuing, traffic scheduling, and QoS requirements.
  • Expertise in cache coherency protocols (AMBA CHI/AXI protocol), DDR/LPDDR/GDDR memory technology, and IO technology (PCIe/CCIX/CXL).
  • Prior experience or strong understanding of traffic patterns for ML/AI algorithms in a heterogeneous computation system is a plus.
  • Prior experience on formal verification of cache coherence protocols is a plus.
  • Proficient in C/C++ programming. Experience in the development of highly efficient C/C++ CPU models.
Responsibilities
  • Collaborate with the software team and platform architecture team to understand fabric bandwidth and latency requirements and real-time constraints for AI accelerator, CPU, security, and networking traffic. Devise QoS and ordering rules among the CPU, accelerator, and IO coherent/non-coherent traffics.
  • Identify representative traffic patterns for the software applications. Perform data-driven analysis to evaluate fabric topology, QoS, memory architecture , and u-architecture solutions to improve performance, power efficiency, or reduce hardware.
  • Create directory-base cache coherency specification to satisfy performance requirements of coherent multiple-cluster CPU system and accelerator. Tradeoff protocol complexity and performance requirements.
  • Design cache hierarchy to create best performance
  • Set SoC architecture direction based on the data analysis and work with a cross-functional team to achieve the best hardware/software solutions to meet PPA goals.
  • Develop a SoC cycle-accurate performance model includes memory sub-systems, directory-based coherence cache controllers, fabric interconnects, and fabric switches that describe the microarchitecture, use it for evaluation of new features.
  • Collaborate with RTL and Physical design engineers to make power, performance, and area trade-offs.
  • Drive analysis and correlation of performance feature both pre and post-silicon.

Tenstorrent specializes in developing high-quality AI/ML accelerators, including individual PCIe cards, workstations, servers, and ultra-dense Galaxy pods, featuring industry-standard chiplets and a modular RISC-V CPU. Their products also incorporate a BUDA software framework designed for open-source collaboration, enabling efficient and scalable hardware for deep learning applications.

Company Stage

Series C

Total Funding

$334.5M

Headquarters

Toronto, Canada

Founded

2016

Growth & Insights
Headcount

6 month growth

15%

1 year growth

39%

2 year growth

125%
Simplify Jobs

Simplify's Take

What believers are saying

  • The launch of next-generation Wormhole-based developer kits and workstations could attract a significant developer community, driving innovation and adoption.
  • Collaborations with industry giants like Hyundai and Rapidus indicate strong growth potential and access to advanced manufacturing technologies.
  • The introduction of specialized AI inference acceleration boards like the Grayskull e75 and e150 can capture a niche market in AI and machine learning applications.

What critics are saying

  • The competitive landscape in AI hardware is intense, with major players like NVIDIA and Intel posing significant challenges.
  • Dependence on strategic partnerships for advanced manufacturing and technology development could lead to vulnerabilities if these partnerships falter.

What makes Tenstorrent unique

  • Tenstorrent's use of RISC-V architecture in their AI processors offers a unique alternative to traditional x86 and ARM architectures, providing flexibility and open-source benefits.
  • Their focus on high-performance AI chips and scalable developer kits positions them as a key player in the AI hardware market, particularly for developers seeking robust multi-chip solutions.
  • Strategic partnerships with global entities like Rapidus and C-DAC enhance their capabilities in cutting-edge semiconductor technology and edge AI processing.

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