Original title: New neuromorphic chip for AI on the edge, at a small fraction of the energy and size of today's computing platforms.

> The NeuRRAM neuromorphic chip brings AI a step closer to running on a broad range of edge devices, disconnected from the cloud ... anywhere and anytime without relying on a network connection to a centralized server[...: ]from smart watches, to VR headsets, smart earbuds, smart sensors in factories and rovers for space exploration

> [Weier Wan, Uni Stanford/Uni SanDiego:] «our NeuRRAM chip obtains efficiency while not sacrificing versatility»

> On AI chips, moving data from memory to computing units is one major bottleneck [...] To solve this data transfer issue, researchers used what is known as resistive random-access memory, a type of non-volatile memory that allows for computation directly within memory rather than in separate computing units. RRAM and other emerging memory technologies used as synapse arrays for neuromorphic computing were pioneered in the lab of Philip Wong, Wan's advisor at Stanford and a main contributor to this work. Computation with RRAM chips [introduced more than 30 years ago] generally... leads to a decrease in the accuracy of the computations performed on the chip and a lack of flexibility in the chip's architecture. [...] "What is new with NeuRRAM is that the extreme efficiency now goes together with great flexibility for diverse AI applications with almost no loss in accuracy over standard digital general-purpose compute platforms"

> EDP [energy-delay product measurement] combines both the amount of energy consumed for every operation and the amount of times it takes to complete the operation[: ]the NeuRRAM chip achieves 1.6 to 2.3 times lower EDP (lower is better) and 7 to 13 times higher computational density than state-of-the-art chips

Some further technical details require particular attention and cannot be easily properly abridged.

Orignal research article: A compute-in-memory chip based on resistive random-access memory - https://www.nature.com/articles/s41586-022-04992-8

> Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory. Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware, it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM — a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0% on MNIST18 and 85.7% on CIFAR-1019 image classification, 84.7% accuracy on Google speech command recognition, and a 70% reduction in image-reconstruction error on a Bayesian image-recovery task