Towards efficient generative AI and beyond-AI computing:New trends on ISSCC 2024 machine learning accelerators

Compared to the last decade when the convolution neu-ral network(CNN)dominated the research field,machine learn-ing(ML)algorithms have reached a pivotal moment called the generative artificial intelligence(AI)era.With the emer-gence of large-scale foundation models[1],such as large multi-modal model(LMM)GPT-4[2]and text-to-image generative model DALL·E[3].

Self-selective memristor-enabled in-memory search for highly efficient data mining

Similarity search,that is,finding similar items in massive data,is a fundamental computing problem in many fields such as data mining and information retrieval.However,for large-scale and high-dimension data,it suffers from high computational complexity,requiring tremendous computation resources.Here,based on the low-power self-selective memristors,for the first time,we propose an in-memory search(IMS)system with two innovative designs.First,by exploiting the natural distribution law of the devices resistance,a hardware locality sensitive hashing encoder has been designed to transform the realvalued vectors into more efficient binary codes.Second,a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel.Our IMS system demonstrated a 168energy efficiency improvement over all-transistors counterparts in clustering and classification tasks,while achieving a software-comparable accuracy,thus providing a low-complexity and low-power solution for in-memory data mining applications.

Toward memristive in-memory computing:principles and applications

With the rapid growth of computer science and big data,the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories.Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues,and plentiful applications have been demonstrated and verified.These applications can be broadly categorized into two major types:soft computing that can tolerant uncertain and imprecise results,and hard computing that emphasizes explicit and precise numerical results for each task,leading to different requirements on the computational accuracies and the corresponding hardware solutions.In this review,we conduct a thorough survey of the recent advances of memristive in-memory computing applications,both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms,and the hard computing type that includes scientific computing and digital image processing.At the end of the review,we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.