neural networks

Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and …

A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator Designed with a High-Productivity VLSI Methodology

This work presents a scalable deep neural network (DNN) inference accelerator consisting of 36 small chips connected in a mesh network on a multi-chip-module (MCM). The accelerator enables flexible scaling for efficient inference on a wide range of …

A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm

This work presents a scalable deep neural network (DNN) accelerator consisting of 36 chips connected in a mesh network on a multi-chip-module (MCM) using ground-referenced signaling (GRS). While previous accelerators fabricated on a single monolithic …