Advancements in Hardware Acceleration and Neural Network Optimization

The field of hardware acceleration and neural network optimization is witnessing significant advancements, particularly in the areas of design space exploration, low-bit quantization, and the integration of temporal information into neural network models. Innovations are focusing on overcoming the limitations of traditional methods through the application of machine learning and novel hardware structures. For instance, learning-based techniques are being developed to navigate the complex and non-uniform design spaces of deep neural network accelerators more efficiently. Similarly, the introduction of extreme low-bit quantization methods is enabling more power and area-efficient hardware designs without substantial accuracy loss. Additionally, the exploration of temporal information in neural networks is opening new avenues for processing event-based data with higher efficiency and lower energy consumption.

Noteworthy papers include:

  • AIRCHITECT v2: Introduces a learning-based design space exploration technique that significantly outperforms existing methods in identifying optimal hardware designs for deep neural networks.
  • LUT-DLA: Proposes a framework for extreme low-bit quantization of neural networks, achieving remarkable improvements in power and area efficiency.
  • Delay Neural Networks (DeNN): Presents a novel class of neural networks that leverage temporal information, showing superior performance on event-based datasets with fewer parameters and less energy consumption.
  • SoMa: Develops a new paradigm for DRAM communication scheduling in DNN accelerators, significantly improving performance and reducing energy costs.
  • Continuous signal sparse encoding using analog neuromorphic variability: Offers a robust and efficient method for encoding continuous signals, suitable for low-power, always-on systems.

Sources

AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations

Delay Neural Networks (DeNN) for exploiting temporal information in event-based datasets

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator

MappedTrace: Tracing Pointer Remotely with Compiler-generated Maps

Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

SoMa: Identifying, Exploring, and Understanding the DRAM Communication Scheduling Space for DNN Accelerators

Late Breaking Result: FPGA-Based Emulation and Fault Injection for CNN Inference Accelerators

Fast-Locking and High-Resolution Mixed-Mode DLL with Binary Search and Dead Clock Detection for Wide Frequency Ranges in 3-nm FinFET CMOS

A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks

Continuous signal sparse encoding using analog neuromorphic variability

Compiler Support for Speculation in Decoupled Access/Execute Architectures

Efficient Synaptic Delay Implementation in Digital Event-Driven AI Accelerators

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