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Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs). However, mixed-signal circuitry suffers from limited range for information encoding, susceptibility to noise, and Analog to Digital (A/D) conversion overheads. This paper aims to address these challenges by offering and leveraging the insight that a vector dot-product (the basic operation in DNNs) can be bit-partitioned into groups of spatially parallel low-bitwidth operations, and interleaved across multiple elements of the vectors. As such, the building blocks of our accelerator become a group of wide, yet low-bitwidth multiply-accumulate units that operate in the analog domain and share a single A/D converter. The low-bitwidth operation tackles the encoding range limitation and facilitates noise mitigation. Moreover, we utilize the switched-capacitor design for our bit-level reformulation of DNN operations. The proposed switched-capacitor circuitry performs the group multiplications in the charge domain and accumulates the results of the group in its capacitors over multiple cycles. The capacitive accumulation combined with wide bit-partitioned operations alleviate the need for A/D conversion per operation. With such mathematical reformulation and its switched-capacitor implementation, we define a 3Dstacked microarchitecture, dubbed BIHIWE 1 -pronounced Bee Hive-that leverages clustering and hierarchical design to best utilize power-efficiency of the mixed-signal domain and 3D stacking. For ten DNN benchmarks, BIHIWE delivers 4.9×speedup over a leading purely-digital 3D-stacked accelerator TETRIS, with a mere of less than 0.5% accuracy loss achieved by careful treatment of noise, computation error, and various forms of variation. Compared to RTX 2080 TI with tensor cores and Titan Xp GPUs, all with 8-bit execution, BI-HIWE offers 33.1×and 66.5×higher Performance-per-Watt, respectively. BIHIWE also outperforms other leading digital and analog accelerators in power efficiency. The results suggest that BIHIWE is an effective initial step in a road that combines mathematics, circuits, and architecture.

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