{'fraction_non_alphanumeric': 0.034161490683229816, 'fraction_numerical': 0.0, 'mean_word_length': 5.7894736842105265, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
RRAM-enabled AI Accelerator Architecture

Resistive random-access memory (RRAM) offers high-density non-volatile storage and potential for efficient in-memory computing (IMC). RRAM-enabled accelerators can solve the von Neumann bottleneck and meet the ever-growing computing needs of applications such as Artificial Intelligence (AI). In this paper, we discuss progress and challenges in RRAM-based accelerators for AI inference, training and arithmetic applications. Architecture strategies to accommodate large neural networks will be introduced. Alternative computing systems enabled by the devices' internal dynamics will also be presented.