docs: add paper link to readme

README.md

@@ -13,6 +13,16 @@
 [![RTL Linting](https://github.com/joennlae/halutmatmul/actions/workflows/hw_linting.yaml/badge.svg)](https://github.com/joennlae/halutmatmul/actions/workflows/hw_linting.yaml)
 [![HW Design Verification](https://github.com/joennlae/halutmatmul/actions/workflows/hw_dv.yaml/badge.svg)](https://github.com/joennlae/halutmatmul/actions/workflows/hw_dv.yaml)
 
+# Paper
+
+* [Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication](https://arxiv.org/abs/2311.10207)
+
+### Abstract
+
+```
+The recent Maddness method approximates Matrix Multiplication (MatMul) without the need for multiplication by using a hash-based version of product quantization (PQ). The hash function is a decision tree, allowing for efficient hardware implementation, as multiply-accumulate operations are replaced by decision tree passes and LUT lookups. Stella Nera is the first Maddness accelerator achieving 15x higher area efficiency (GMAC/s/mm^2) and 25x higher energy efficiency (TMAC/s/W) than direct MatMul accelerators in the same technology. In a commercial 14 nm technology and scaled to 3 nm, we achieve an energy efficiency of 161 TOp/s/W@0.55V with a Top-1 accuracy on CIFAR-10 of over 92.5% using ResNet9.
+```
+
 # Algorithmic - Maddness
 
 ![Maddness Animation](/docs/images/maddness_animation.webp)