EAS-CiM 2.0: Event-driven Asynchronous Stream-based Compute-in-Memory Kernels with Scalable Precision

Traditional edge accelerators struggle to balance performance, accuracy, and power consumption. Our solution leverages Asynchronous Stream Computing (ASC) to implement a fully asynchronous, clockless system where data is encoded in the frequency and duty cycle of streams. This architecture adapts dynamically to available energy resources, adjusting computational precision and speed in response to workload demands. A stimulus-driven model allows the system to process data efficiently when actionable information is present. Analytical and experimental results highlight a tunable trade-off between latency and precision, aligning system performance with real-time requirements. To validate these concepts, we implement a Compute-in-Memory (CiM) based dot product architecture to accelerate Word2Vec Algorithms for Natural Language Processing (NLP) tasks and have exhibited a relative semantic accuracy scaling of 47.81% and a computational efficiency scaling of 43.23x when scaling precision from 12 - 4 bits.