Vector quantization (VQ) is a general data compression technique that has a scalable implementation complexity and potentially a high compression ratio. In this paper, a novel implementation of VQ using stochastic circuits is proposed and its performance is evaluated against conventional binary designs. The stochastic and binary designs are compared for the same compression quality, and the circuits are synthesized for an industrial 28-nm cell library. The effects of varying the sequence length of the stochastic representation are studied with respect to throughput per area (TPA) and energy per operation (EPO). The stochastic implementations are shown to have higher EPOs than the conventional binary implementations due to longer latencies. When a shorter encoding sequence with 512 bits is used to obtain a lower quality compression measured by the    $L^{1}$   -norm, squared    $L^{2}$   -norm, and third-law errors, the TPA ranges from 1.16 to 2.56 times than that of the binary implementation with the same compression quality. Thus, although the stochastic implementation underperforms for a high compression quality, it outperforms the conventional binary design in terms of TPA for a reduced compression quality. By exploiting the progressive precision feature of a stochastic circuit, a readily scalable processing quality can be attained by halting the computation after different numbers of clock cycles.
['Ran Wang', 'Jie Han', 'Bruce F. Cockburn', 'Duncan G. Elliott']
Stochastic Circuit Design and Performance Evaluation of Vector Quantization for Different Error Measures