Neuromorphic Computing and Quantum-Inspired Algorithms

Advances in Neuromorphic Computing and Quantum-Inspired Algorithms

The recent developments in the field of neuromorphic computing and quantum-inspired algorithms are pushing the boundaries of both hardware and software innovations. Neuromorphic computing is seeing significant advancements with the integration of novel devices and materials, aiming to bridge the gap between CMOS-based systems and emerging technologies. This integration is crucial for the realization of large-scale, functional neuromorphic systems capable of on-chip learning, as highlighted by the introduction of TEXEL, a mixed-signal neuromorphic architecture designed for such purposes.

In the realm of quantum-inspired algorithms, there is a growing focus on leveraging quantum principles to enhance classical computational tasks. This includes the application of tensor network methods to solve combinatorial optimization problems, as demonstrated in the factorization of RSA numbers up to 100 bits. These methods, while not yet posing a threat to current cryptographic standards, underscore the potential of quantum-inspired techniques to revolutionize computational efficiency.

Memristive systems are also making strides, particularly in the context of in-memory computing and large language models (LLMs). The deployment of LLMs on memristor crossbars is a promising direction, addressing challenges such as model size and computational complexity. Innovations in this area are not only enhancing energy efficiency but also paving the way for more compact and powerful AI systems.

Noteworthy papers in this space include:

  • TEXEL: Demonstrates the readiness of neuromorphic processors for beyond-CMOS device integration.
  • Quantum-inspired factorization: Highlights the potential of quantum-inspired techniques in classical computational tasks.
  • LLM deployment on memristor crossbars: Presents a novel architecture that significantly enhances the efficiency of LLMs.

These developments collectively indicate a shift towards more efficient, scalable, and powerful computational paradigms, driven by advancements in both hardware and algorithmic fronts.

Sources

Multi-diseases detection with memristive system on chip

A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI

Assessing Quantum Extreme Learning Machines for Software Testing in Practice

Design of a 64-bit SQRT-CSLA with Reduced Area and High-Speed Applications in Low Power VLSI Circuits

ATOMIC: Automatic Tool for Memristive IMPLY-based Circuit-level Simulation and Validation

Integer Polynomial Factorization by Recombination of Real Factors: Re-evaluating an Old Technique in Modern Era

TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration

Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small

1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs

On-Device LLMs for SMEs: Challenges and Opportunities

QML-IDS: Quantum Machine Learning Intrusion Detection System

Quantum inspired factorization up to 100-bit RSA number in polynomial time

High-Order Associative Learning Based on Memristive Circuits for Efficient Learning

Truncated multiplication and batch software SIMD AVX512 implementation for faster Montgomery multiplications and modular exponentiation

Built with on top of