# TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks

arXiv: https://arxiv.org/abs/1909.06892 · HTML: https://mlsanity.com/paper/1909.06892/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification](https://mlsanity.com/paper/2205.07877/) (2022)
- [Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision](https://mlsanity.com/paper/2411.01431/) (2024)
- [xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems](https://mlsanity.com/paper/2405.19065/) (2024)
- [FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks](https://mlsanity.com/paper/2201.07634/) (2022)
- [Ternary Quantization: A Survey](https://mlsanity.com/paper/2303.01505/) (2023)
- [Search-Optimized Quantization in Biomedical Ontology Alignment](https://mlsanity.com/paper/2507.13742/) (2025)
- [GAVINA: flexible aggressive undervolting for bit-serial mixed-precision DNN acceleration](https://mlsanity.com/paper/2511.23203/) (2025)

## Abstract (arXiv)

The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs). In the quest for lower precision, recent studies have shown that ternary DNNs (which represent weights and activations by signed ternary values) represent a promising sweet spot, achieving accuracy close to full-precision networks on complex tasks. We propose TiM-DNN, a programmable in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted {-1,0,1}, symmetric weighted {-a,0,a}, and asymmetric weighted {-a,0,b} ternary systems. The building blocks of TiM-DNN are TiM tiles -- specialized memory arrays that perform massively parallel signed ternary vector-matrix multiplications with a single access. TiM tiles are in turn composed of Ternary Processing Cells (TPCs), bit-cells that function as both ternary storage units and signed ternary multiplication units. We evaluate an implementation of TiM-DNN in 32nm technology using an architectural simulator calibrated with SPICE simulations and RTL synthesis. We evaluate TiM-DNN across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks. A 32-tile instance of TiM-DNN achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W and TOPS/mm2, respectively, compared to an NVIDIA Tesla V100 GPU. In comparison to specialized DNN accelerators, TiM-DNN achieves 55X-240X and 160X-291X improvement in TOPS/W and TOPS/mm2, respectively. Finally, when compared to a well-optimized near-memory accelerator for ternary DNNs, TiM-DNN demonstrates 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup, underscoring the potential of in-memory computing for ternary DNNs.