  Deep-learning accelerators are increasingly in demand; however, their
performance is constrained by the size of the feature map, leading to high
bandwidth requirements and large buffer sizes. We propose an adaptive scale
feature map compression technique leveraging the unique properties of the
feature map. This technique adopts independent channel indexing given the weak
channel correlation and utilizes a cubical-like block shape to benefit from
strong local correlations. The method further optimizes compression using a
switchable endpoint mode and adaptive scale interpolation to handle unimodal
data distributions, both with and without outliers. This results in 4$\times$
and up to 7.69$\times$ compression rates for 16-bit data in constant and
variable bitrates, respectively. Our hardware design minimizes area cost by
adjusting interpolation scales, which facilitates hardware sharing among
interpolation points. Additionally, we introduce a threshold concept for
straightforward interpolation, preventing the need for intricate hardware. The
TSMC 28nm implementation showcases an equivalent gate count of 6135 for the
8-bit version. Furthermore, the hardware architecture scales effectively, with
only a sublinear increase in area cost. Achieving a 32$\times$ throughput
increase meets the theoretical bandwidth of DDR5-6400 at just 7.65$\times$ the
hardware cost.
