Encoding

MITTA: A Multi-Task Transformer Accelerator With Mixed Precision Structured Sparsity and Hierarchical Task-Adaptive Power Management

MITTA: A Multi-Task Transformer Accelerator With Mixed Precision Structured Sparsity and Hierarchical Task-Adaptive Power Management 150 150

Abstract:

This article presents MITTA, the first silicon-proven transformer accelerator optimized for multi-task inference across both natural language processing (NLP) and image processing domains. MITTA accelerates a task-sharing algorithm that minimizes sub-task computation by reusing both activations and weights from a shared base task, requiring only sparse delta computation for sub-tasks. …

View on IEEE Xplore

A 7-Level 18-Wire-State Trio-Signaling Transmitter for MIPI C-PHY 3.0 Interfaces

A 7-Level 18-Wire-State Trio-Signaling Transmitter for MIPI C-PHY 3.0 Interfaces 150 150

Abstract:

This letter presents a MIPI C-PHY v3.0 TX, which adopts trio-signaling using three wires per lane. Each line supports seven-level signaling, enabling 18 wire states to map 32-bit data into nine symbols, achieving 3.56 bits/symbol efficiency. Balanced coding maintains constant driver current, enhancing SSO noise immunity, and embedded clocking is achieved …

View on IEEE Xplore

A Machine Learning-Inspired PAM-4 Transceiver for Medium-Reach Wireline Links

A Machine Learning-Inspired PAM-4 Transceiver for Medium-Reach Wireline Links 150 150

Abstract:

This article presents an energy-efficient machine learning-inspired PAM-4 wireline transceiver that leverages data encoding at the transmitter (Tx) and feature extraction with classification at the receiver (Rx) to compensate for channel loss ranging from 13 to 26 dB, while maintaining the bit error rate (BER)<10-11. A new consecutive symbol-to-center (CSC) encoding …

View on IEEE Xplore

ROZK: An Energy-Efficient DNN Accelerator Based on Reconfigurable NoC and Local Zero-Skipping

ROZK: An Energy-Efficient DNN Accelerator Based on Reconfigurable NoC and Local Zero-Skipping 150 150

Abstract:

Zero-skipping is a famous technique to improve the energy efficiency of deep neural network (DNN) accelerators. When the zero-skipping is realized with encoded data using lossless compression, irregular and unpredictable size of data due to inconsistent compression rate incurs several design issues including: 1) load imbalance from irregularity of data stored …

View on IEEE Xplore