With the increasing cost of global communication on-chip, high-performance designs for data-intensive applications require architectures that distribute hardware resources (computing logic, memories, interconnect, etc.) throughout a chip, while restricting computations and communications to geographic proximities. In this paper, we present a methodology for high-level synthesis (HLS) of distributed logic-memory architectures, i.e., architectures that have logic and memory distributed across several partitions in a chip. Conventional HLS tools are capable of extracting parallelism from a behavior for architectures that assume a monolithic controller/datapath communicating with a memory or memory hierarchy. This work provides techniques to extend the synthesis frontier to more general architectures that can extract both coarse- and fine-grained parallelism from data accesses and computations in a synergistic manner. Our methodology selects many possible ways of organizing data and computations, carefully examines the trade-offs (i.e., communication overheads, synchronization costs, area overheads) in choosing one solution over another, and utilizes conventional HLS techniques for intermediate steps. We have evaluated the proposed framework on several benchmarks by generating register-transfer level (RTL) implementations using an existing commercial HLS tool with and without our enhancements, and by subjecting the resulting RTL circuits to logic synthesis and layout. The results show that circuits designed as distributed logic-memory architectures using our framework achieve significant (up to 5.31×, average of 3.45×) performance improvements over well-optimized conventional designs with small area overheads (up to 19.3%, 15.1% on average).