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

The use of lower precision to perform computations has emerged as a popular technique to enable complex Deep Neural Networks (DNNs) to be realized on energy-constrained platforms. In the quest for lower precision, studies to date have shown that ternary DNNs, which represent weights and activations by signed ternary values, represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification. We propose TiM-DNN, a programmable hardware accelerator that is specifically designed to execute state-of-the-art 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. TiM-DNN is an in-memory accelerator designed using TiM tiles - specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values per access. TiM tiles are in turn composed of Ternary Processing Cells (TPCs), new bit-cells that function as both ternary storage units and signed scalar multiplication units. We evaluate an implementation of TiM-DNN in 32nm technology using an architectural simulator calibrated with SPICE simulations and RTL synthesis. 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 a state-of-the-art NVIDIA Tesla V100 GPU. In comparison to popular DNN accelerators, TiM-DNN achieves 55.2X-240X and 160X-291X improvement in TOPS/W and TOPS/mm2, respectively. We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup.