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New compute-in-memory macro boosts edge AI inference efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed E-ReCON, a novel compute-in-memory (CIM) macro designed for efficient AI inference on edge devices. This macro utilizes a compact ReRAM bitcell capable of performing multiplication for both conventional neural networks and spiking neural networks. The design incorporates an interleaved adder tree to reduce transistor count and power consumption, achieving high energy efficiency and low latency. AI

IMPACT This new compute-in-memory macro could enable more powerful and energy-efficient AI processing directly on edge devices.

RANK_REASON The cluster contains a research paper detailing a new hardware macro for AI inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New compute-in-memory macro boosts edge AI inference efficiency

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The cluster contains a research paper detailing a new hardware macro for AI inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Santosh Kumar Vishvakarma ·

    E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference

    This work presents E-ReCON, a 16 Kb energy and resource-efficient digital compute-in-memory (DCIM) macro based on a compact 3T1R ReRAM bitcell for edge-AI inference. The proposed bitcell occupies only 0.85 um^2 and supports reliable AND-based in-memory multiplication for both con…

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