High Efficiency Multiply-Accumulator Using Ternary Logic and Ternary Approximate Algorithm

A multiply-accumulator, often abbreviated as a MAC unit, is central to a multitude of computational tasks, particularly those tasks (such as neural networks) involving array-based mathematical computations. The quest for novel methods to efficiently store and process data in a MAC has become imperative. Recently, ternary logic has attracted significant attention due to its higher information density than conventional binary systems. However, though numerous studies have showcased ternary arithmetic circuits, advancements in ternary-based vector processing have been notably scarce. To bridge this gap, this work undertakes comprehensive study into the optimization of ternary MAC units. Firstly, we propose various ternary approximate algorithms which shows 30%-less power consumption and only 2% computation error when compared with the accurate design. Secondly, we design sophisticated ternary circuits and obtain 74%∼80% lower power-delay-product (PDP) than previous works. Finally, we evaluate the proposed ternary MAC unit using both carbon-nanotube field-effect transistor (CNTFET) and silicon-based 180 nm CMOS processes. The simulation results show the ternary circuit is better than binary circuit in terms of both area (∼45% less) and power (∼30% less), highlighting its strong potential for practical applications.