  Hardware specialization is becoming a key enabler of energyefficient
performance. Future systems will be increasingly heterogeneous, integrating
multiple specialized and programmable accelerators, each with different memory
demands. Traditionally, communication between accelerators has been
inefficient, typically orchestrated through explicit DMA transfers between
different address spaces. More recently, industry has proposed unified coherent
memory which enables implicit data movement and more data reuse, but often
these interfaces limit the coherence flexibility available to heterogeneous
systems. This paper demonstrates the benefits of fine-grained coherence
specialization for heterogeneous systems. We propose an architecture that
enables low-complexity independent specialization of each individual coherence
request in heterogeneous workloads by building upon a simple and flexible
baseline coherence interface, Spandex. We then describe how to optimize
individual memory requests to improve cache reuse and performance-critical
memory latency in emerging heterogeneous workloads. Collectively, our
techniques enable significant gains, reducing execution time by up to 61% or
network traffic by up to 99% while adding minimal complexity to the Spandex
protocol.
