TSMC-posted 2 days ago
$42 - $48/Yr
Intern
5,001-10,000 employees

Design an edge accelerator for GenAI, based on https://dl.acm.org/doi/full/10.1145/3768168 Evaluate PPA by RTL at N7 for all necessary building blocks Evaluate and benchmark different memory options, including various eDRAM and eNVM In case of eDRAM, quantify the refresh overhead for various options Evaluate various 3D array design options by considering the integration scheme (bonding, monolithic, hybrid) and granularity of 3D connections Evaluate the benefits of 3D interconnect pitch scaling Analyze the viability of the low-power memory access schemes at extreme bandwidth proposed in the reference and offer solutions tailored for specific memory devices and arrays Quantify the differences between the new design and the analytical estimation in the reference Include workload-level energy and latency benchmarks for the considered technology options Show the pathway for upscaling to larger workloads either by larger monolithic dies or 2.5D chiplet integration

  • Design an edge accelerator for GenAI
  • Evaluate PPA by RTL at N7 for all necessary building blocks
  • Evaluate and benchmark different memory options, including various eDRAM and eNVM
  • Quantify the refresh overhead for various eDRAM options
  • Evaluate various 3D array design options
  • Evaluate the benefits of 3D interconnect pitch scaling
  • Analyze the viability of low-power memory access schemes
  • Quantify the differences between the new design and the analytical estimation in the reference
  • Include workload-level energy and latency benchmarks for the considered technology options
  • Show the pathway for upscaling to larger workloads
  • Ph.D. Student in Electrical Engineering or Computer Science
  • Experience on: RTL design
  • Experience on: Embedded DRAM options
  • Experience on: 3D memory array evaluations
  • Experience on: Generative AI workloads
  • Experience on: Accelerator latency and energy evaluations
  • Embedded NVM options and evaluations
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