In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.

We introduce TinyGarble, a novel automated methodology based on powerful logic synthesis techniques for generating and optimizing compressed Boolean circuits used in secure computation, such as Yao's Garbled Circuit (GC) protocol. TinyGarble achieves an unprecedented level of compactness and scalability by using a sequential circuit description for GC. We introduce new libraries and transformations, such that our sequential circuits can be optimized and securely evaluated by interfacing with available garbling frameworks. The circuit compactness makes the memory footprint of the garbling operation fit in the processor cache, resulting in fewer cache misses and thereby less CPU cycles. Our proof-of-concept implementation of benchmark functions using TinyGarble demonstrates a high degree of compactness and scalability. We improve the results of existing automated tools for GC generation by orders of magnitude; for example, TinyGarble can compress the memory footprint required for 1024-bit multiplication by a factor of 4,172, while decreasing the number of non-XOR gates by 67%. Moreover, with TinyGarble we are able to implement functions that have never been reported before, such as SHA-3. Finally, our sequential description enables us to design and realize a garbled processor, using the MIPS I instruction set, for private function evaluation. To the best of our knowledge, this is the first scalable emulation of a general purpose processor.

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We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring Z 2 l using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.

This paper introduces the first efficient, scalable, and practical method for privacy-preserving k-nearest neighbors (k-NN) search. The approach enables performing the widely used k-NN search in sensitive scenarios where none of the parties reveal their information while they can still cooperatively find the nearest matches. The privacy preservation is based on the Yao's garbled circuit (GC) protocol. In contrast with the existing GC approaches that only accept function descriptions as combinational circuits, we suggest using sequential circuits. This work introduces novel transformations, such that the sequential description can be evaluated by interfacing with the existing GC schemes that only accept combinational circuits. We demonstrate a great efficiency in the memory required for realizing the secure k-NN search. The first-of-a-kind implementation of privacy preserving k-NN, utilizing the Synopsys Design Compiler on a conventional Intel processor demonstrates the applicability, efficiency, and scalability of the suggested methods.