Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators | ScienceToStartup | ScienceToStartup

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References (51)

[1]

In-Situ NAS: A Plug-and-Search Neural Architecture Search Framework Across Hardware Platforms

2025Hao Lv, Lei Zhang et al.

[2]

Efficient and Robust Edge AI: Software, Hardware, and the Co-design

2025Bokyung Kim, Shiyu Li et al.

[3]

A full-stack memristor-based computation-in-memory system with software-hardware co-development

Founder's Pitch

"Develop a co-optimization framework for designing generalized in-memory computing accelerators that efficiently support multiple neural network workloads."

Hardware Optimization•Score: 5•View PDF ↗

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Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators

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References (51)

Founder's Pitch

"Develop a co-optimization framework for designing generalized in-memory computing accelerators that efficiently support multiple neural network workloads."

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Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators

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Founder's Pitch

"Develop a co-optimization framework for designing generalized in-memory computing accelerators that efficiently support multiple neural network workloads."

Commercial Viability Breakdown

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