Memory Wall is not gone: A Critical Outlook on Memory Architecture in Digital Neuromorphic Computing

This project is a theoretical research paper rather than a software tool, resulting in a low defensibility score as there is no code-based moat or active ecosystem (0 stars, 3 forks indicating internal/academic usage). It addresses a niche but critical hardware engineering problem: the 'Memory Wall' in neuromorphic chips. While frontier labs like OpenAI have invested in neuromorphic startups (e.g., Rain AI), they are unlikely to build competing analytical frameworks for this specific hardware niche, focusing instead on transformer-optimized silicon (H100/B200 equivalents). The paper's value lies in its critical outlook on area consumption by SRAM and STT-MRAM, which challenges the industry narrative that distributed computing solves the memory bottleneck. Competitors include academic research groups and R&D divisions of IBM (TrueNorth) or Intel (Loihi). As a survey/analysis, it serves as a reference for hardware designers rather than a platform that can be dominated or consolidated.